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\n
1. Introduction
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Malignant mesothelioma (MM) is a fatal disease which originates in the mesothelial surfaces of pleura or, more rarely, in other sites such as peritoneum. Most cases have been classically linked to asbestos exposure; however, ionizing radiation may also increase the risk of mesothelioma [1].
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Its prognosis is very poor and it is difficult to treat, mainly because most patients are diagnosed with advanced disease [1, 2, 3]. Despite clinical research efforts, lack of available therapies remains clear and median overall survival is still approximately 1 year, with only 10% patients alive 5 years after diagnosis. Standard of care treatments and guidelines have not been evolved much along recent years. In this chapter, NCCN and ESMO guidelines have been reviewed, besides an electronic search of the Pubmed database, with a focus on the phase II and III clinical trials, guidelines, meta-analysis, and systematic reviews regarding epidemiology, diagnosis tests, surgical approach, and approved local and systemic treatments, including most important advances. Searched terms included “mesothelioma,” “ESMO and NCCN guidelines,” “diagnosis,” “surgery,” “targeted therapy,” “clinical trials,” “palliative treatment,” and “meta-analysis.” First-line regimen recommendations have not evolved since the phase III pivotal study of cisplatin-pemetrexed was published, and this combination became the standard of care despite its modest benefit in survival. Pemetrexed seems to be the most active drug, but its use in the first-line setting limits its administration in further lines. However, a rechallenge may be done in responder patients, who might still get benefit [4].
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Only few drugs have demonstrated a mild activity in refractory MM, and targeted therapies have provided disappointing results so far. However, recent clinical trial data with immunotherapies are bringing some light and may become a new paradigm in the following years.
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2. Epidemiology
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Malignant mesothelioma (MM) is a rare tumor, with an incidence of less than 5 out of 100,000 inhabitants in Europe [1]. Diagnosis is usually done when disease is well advanced, and patients have a high symptom burden [3]. Incidence has decreased along the last decades globally worldwide. Mesothelioma has been typically related to asbestos exposure, which is the most well-known risk factor, although the latency period can be long, with a latency period being approximately 40 years, although in some cases, it may be as long as 60–70 years. Recent reports have suggested that also ionizing radiation may have a role, such as in patients previously treated with radiotherapy (RT). Other studies also suggest that erionite (which may be found in travel roads) increases the risk of MM. Smoking is not a risk factor. There may be a genetic risk in patients with BRCA-1 mutation [5, 6, 7].
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The most common type of mesothelioma is malignant pleural mesothelioma, being up to 70% cases, followed by peritoneal (30%) and pericardial mesothelioma (1–2%) [2]. According to histology, there are three subtypes: epithelial, sarcomatoid, and biphasic [3], with epithelial subtype having a better prognosis.
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Prevalence is highly linked to mortality, and mesothelioma is an unmet medical need due to its very poor prognosis, having a median overall survival of approximately 9–12 months, with only very modest improvements in survival over time [8].
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3. Diagnosis
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Most common symptoms include dyspnea, thoracic pain, and weight loss. Usually unilateral effusions are observed. A detailed occupational history is key, checking asbestos exposure among other previously exposed potential risk factors. Patients often present with advanced disease, but without distant metastases, as local implants or effusion cause pain and/or dyspnea. Brain metastases are rare [3].
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Diagnosis assessments include chest X-ray, computed tomography (CT) scan of chest and upper abdomen, and thoracentesis, with examination of the pleural effusion and general laboratory blood tests [1]. Citology samples from pleural effusion are frequently negative or inconclusive, hence, histology may bring some further light for a more accurate diagnosis. Some biomarkers may be helpful, including calretinin, WT-1, D2-40, and citokeratyn 5/6, being negative in mesothelioma and positive in lung adenocarcinoma [9]. In order to obtain adequate histology, a thoracoscopy is highly recommended to optimally stage and to allow pleural fluid evacuation (with or without pleurodesis) [9, 10]. Mesothelioma can be difficult to identify and distinguish from benign pleural lesions and from other malignancies; it is therefore recommended to obtain biopsies from the tissue of both abnormal and normal appearance. When a thoracoscopy is not feasible or contraindicated, ultrasound-guided true-cut biopsies are a good alternative [10].
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4. Pathology
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MM comprises a heterogeneous group of tumors, which are mainly classified as three subtypes (epithelioid, biphasic, and sarcomatoid), despite the numerous variants that are described in the 2004 WHO classification [9].
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Diagnosis samples may be obtained from pleural effusions, pleural biopsies, and surgical samples [1, 8, 9, 10]. Cytological diagnosis from effusion samples may be feasible, but sensitivity is highly variant, with variable atypia (usually low grade). Therefore, usually tissue biopsies with immunohistochemistry analysis are pivotal for confirmatory diagnosis.
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Standardly used and most recommended biomarkers for diagnosis include calretinin, cytokeratin 5/6, WT1, and podoplanin (D240). For non-small cell adcenocarcinoma, the most useful markers are TTF1, CEA, and EP4 [8].
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5. Staging
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Staging procedures are aimed to describe anatomical extent correlating with prognostic features, which is key in order to make treatment decisions. Standard procedures for staging include chest and abdomen CT with contrast and PET/CT (for those patients who may undergo surgery). Video-assisted thoracoscopy (VATS) is recommended if contralateral disease is suspected [3].
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Patients should be evaluated by a multidisciplinary committee, including oncologist, radiation oncologist, pathologist, pulmonologist, diagnostic imaging specialist, and surgeon.
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The limitation of most classifications is their inaccuracy in describing tumor (T-) and node (N-) extent. The most recent staging system was presented by the International Mesothelioma Interest Group (IMIG) [11]. However, it failed to be an independent prognostic factor when analyzed in the clinical setting using multivariate analysis [11, 12, 13, 14]. Hence, further workup is needed in order to get an accurate and prognostic staging system.
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If a surgical resection is planned, either mediastinoscopy or endobronchial ultrasound of mediastinal lymph nodes are recommended [15]. Besides, two additional tests may be useful if suggested by imaging: laparoscopy in order to rule out any transdiaphragmatic extension and chest MRI to check vascular involvement [14, 15, 16, 17].
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6. Treatment for mesothelioma
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6.1 First-line therapy for mesothelioma
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Chemotherapy is recommended as the sole therapy for patients with ECOG 0–2 who are not amenable for surgery. For patients with ECOG 3–4, best supportive care is strongly recommended.
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Chemotherapy has a role in the palliative treatment of advanced mesothelioma, getting an improvement of symptoms and modest benefit in survival. Standard first-line treatment is based on platinum doublets, with either pemetrexed or raltitrexed [18, 19], being cisplatin/pemetrexed the only FDA-approved regimen. This combination was investigated in a phase III trial comparing cisplatin/pemetrexed vs. cisplatin monotherapy, getting a benefit in survival by 2.8 months (12.1 vs. 9.3 months, P = 0.02) [18].
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Carboplatin may be used as an alternative to cisplatin, particularly in fragile patients, with no significant differences in survival and a better safety profile [20, 21].
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Clinical research has been trying to look for an improvement with the addition of several agents; however, several phase II trials have failed to demonstrate improvement over standard treatment with the addition of antioangiogenics such as bevacizumab or sunitinib [22, 23]. However, a phase III trial compared cisplatin/pemetrexed with or without bevacizumab in patients who were suitable for receiving bevacizumab (ECOG 0–2 with no history of bleeding or thrombosis). Experimental arm was better in terms of survival, with a benefit by 2.7 months (18.8 vs. 16.1 months, P = 0.0167). Grade 3–4 adverse events were more common in the experimental arm, 71 vs. 62%, with more cases of hypertension, grade 3 proteinuria and grade 3–4 thromboembolic events in the bevacizumab arm. The NCCN guidelines then recommends cisplatin/pemetrexed plus bevacizumab followed by maintenance bevacizumab in patients without contraindications [24].
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6.2 Second-line therapy for mesothelioma
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There is a lack of treatment options in the second line and beyond setting, this being an important medical need with no standard of care yet. Pemetrexed as single agent when compared with the best supportive care was not able to provide an improvement in survival [25]. Vinorelbine showed a benefit in terms of responses in several small phase II trials [26].
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Both immunotherapies and targeted therapies are under evaluation as well, but they have not been yielded into approval [27, 28]. In the absence of the standard second-line or further-line therapy, it is recommended that patients are enrolled into clinical trials. Recent data suggest that checkpoint inhibitors may have a role in this setting, with a response rate slightly higher than that previously obtained by other agents [3].
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Checkpoint inhibitors target the programmed death-1 (PD-1) receptor, which improves tumor immunity. Both nivolumab and pembrolizumab target PD-1 receptors, but testing this receptor is not required [29].
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6.3 Immunotherapy and targeted therapies
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Some immunotherapies have been tested or are under clinical development for MPM, including antibodies blocking immune checkpoints that function as negative regulators of T-cell function, cytotoxic T-lymphocyte-associated antigen 4 (CTLA4), programmed death 1 (PD-1), and programmed death ligand 1 (PD-L1). However, there is still a lack of strong support for their use.
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In two nonrandomized studies, the anti-CTLA4 antibody tremelimumab showed preliminary evidence of activity in patients with previously treated mesothelioma [28, 30]. Thereafter, a randomized, placebo-controlled study investigated tremelimumab in patients with mesothelioma (the DETERMINE trial). This trial did not meet the primary end point of OS, as we did not find statistically significant differences in OS between the tremelimumab group [median OS 7.7 months (95% CI: 6.8–8.9)] and the placebo group [median OS 7.3 months (95% CI: 5.9–8.7)] [31].
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In the KEYNOTE-028 trial, previously treated patients with PD-L1-positive MPM received pembrolizumab 10 mg/kg every 2 weeks for up to 2 years or until confirmed progression or unacceptable toxicity. Five of 25 patients (20%) had a partial response (objective response rate of 20%) and 13 (52%) patients had stable disease. Additionally, there was a maintained clinical benefit, with a median duration of response 12.0 months (95% CI: 3.7 not reached) [32, 33]. The NivoMes study, which evaluated nivolumab in unselected patients with previously treated mesothelioma reported response rates of 28%. The JAVELIN study of the anti-PDL-1 antibody avelumab in unselected patients with previously treated mesothelioma reported a response rate of 9.4% with a median PFS of 17.1 weeks. Subgroup analysis in the PD-L1-positive population (cutoff > 5%) showed a response rate of 14% [34]. Novel vaccine approaches using MPM neoantigens identified by gene sequencing are also entering clinical trial on the basis of early animal studies [33].
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As a summary, preliminary data on PD-1- and PD-L1-targeting monoclonal antibodies in MPM suggest that immunotherapy with single agents may have some benefit, possibly because of its complex biology.
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6.4 Radiotherapy
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Administering RT to the entire pleural surface without damaging radiosensitive sites and keeping a good safety profile is very challenging. Radiotherapy (RT) is used in different settings as treatment for MM: palliative, adjuvant, and as part of a multimodality treatment.
\n
As palliative treatment for pain relief bronchial obstruction or other disease related symptoms, there is no strong evidence to support its use; however, it may be recommended in cases of infiltration of the chest wall, administered in short courses such as 1 × 10 or 3 × 8 Gy [35], always understanding that dose of radiation should be based on its purpose.
\n
\n
6.4.1 Pre- and postoperative RT
\n
Limited evidence is available, extracted from retrospective studies only. In general results are poor, in terms of disease control rate, because of the complex growth patterns of the disease. Furthermore, its safety profile is poor due to the wide field size and neighboring vital organs. The introduction of intensity-modulated RT (IMRT) seem to overcome most of these issues and allow the remaining tumor tissue to be properly irradiated. Preliminary results adjuvant IMRT seemed particularly promising. Further studies are needed to better establish the role of RT. Recent studies have underlined the importance of RT technique, both in terms of local control and toxicity. It is therefore recommended that RT is delivered in specialized centers (expert advice) [36, 37].
\n
\n
\n
\n
6.5 Surgery
\n
Surgery may be recommended for patients with stage I to IIIA disease who are in good conditions and are medically operable. A careful assessment before proceeding to surgery is strongly recommended [1, 3].
\n
Objectives of surgery are staging, palliative, and, more uncommonly, curative intent.
\n
\n
6.5.1 Surgery with radical intent
\n
It cannot be considered to have a real radical intention, as its objective is actually obtaining a macroscopic resection removing as much tumor as possible since it is virtually impossible to obtain free resection margins [1]. It can include pleurectomy/decortication (complete removal of involved pleural and all gross tumor) or extrapleural pneumonectomy, including in bloc resection of pleura, lung diaphragm, and often also part of pericardium [38].
\n
Some studies assessed a second-step surgery, following an induction chemotherapy, which is reported as a trimodality approach. Different combined modality regimens have been investigated.
\n
The European Organization for Research and Treatment of Cancer (EORTC) analyzed trimodality therapy in a phase II trial (EORTC 08031). Patients with MM (up to stage cT3N1M0) received induction chemotherapy (cisplatin and pemetrexed × 3) followed by surgery within 21–56 days. Forty-two out of 57 (73.7%) included patients could undergo surgery. Survival figures were positive, with an overall survival of 18.4 months and 13.9 months progression-free survival. Operative mortality was 6.4% [39].
\n
Other phase II trial with a similar design was performed in the USA and included 77 patients, achieving an overall survival of 16.8 months, with an operative mortality of 7% [40].
\n
Although trimodal therapy seemed feasible in selected patients with promising results, it was further evaluated in a phase III trial in the UK with negative results (MARS1 study). In this trial, mortality was as high as 18.8%, with only 45% patients undergoing surgery after induction treatment, and with a lower survival for patients undergoing surgery compared to the control arm where patients received only the induction therapy (14 vs. 19 months) [41].
\n
However, a systematic review performed afterward, including 34 studies from 26 institutions, found highly variant results, with the median survival ranging from 9.4 to 27.5 months and surgical morbidity from 22 to 82%. Probably, it may be explained by different surgical approaches, variability in terms of surgeon’s prior experience, and heterogeneity of included patients, but some patients may get benefit from this treatment [42]. A multidisciplinary team with sufficient experience should provide recommendations on the suitability of patients for trimodality therapy.
\n
\n
\n
6.5.2 Surgery for staging and palliation
\n
Control pleural effusion, talc poudrage, or even decortication in a captured lung may be performed through surgery. One study compared VATS (partial) pleurectomy vs. standard talc poudrage in 196 patients. There was no benefit in terms of survival, but control of pleural effusion and quality of life were significantly better for experimental arm at 6 and 12 months [43].
\n
\n
\n
\n
\n
7. Conclusions
\n
This chapter shows a review of both NCCN and ESMO guidelines besides PubMed available literature. Mesothelioma is one of those tumors with less advanced in the recent years, probably due to its aggressive nature and the limited incidence, which makes clinical research more time consuming. This is considered still as a medical need due to the lack of treatment options beyond the second line. However, research is improving and some immunooncology agents have started to show a small but significant benefit in terms of survival.
\n
\n
Conflict of interest
The author declares no conflict of interest.
\n',keywords:"malignant mesothelioma, chemotherapy, pemetrexed, immunotherapy, clinical trials, nivolumab, pembrolizumab, targeted therapy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/73155.pdf",chapterXML:"https://mts.intechopen.com/source/xml/73155.xml",downloadPdfUrl:"/chapter/pdf-download/73155",previewPdfUrl:"/chapter/pdf-preview/73155",totalDownloads:126,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"April 20th 2020",dateReviewed:"August 10th 2020",datePrePublished:"September 8th 2020",datePublished:"November 11th 2020",dateFinished:null,readingETA:"0",abstract:"Mesothelioma is considered as a rare tumor originating in the mesothelial surfaces of pleura or, more rarely, in other sites such as peritoneum, which harbors a very poor prognosis. Despite clinical research efforts, lack of available therapies remains clear. Standard of care treatments and guidelines have not been evolved much along recent years. In this chapter, main guidelines will be reviewed, besides a systematic Pubmed review, with a focus on epidemiology, diagnosis tests, and approved local and systemic treatments, including most important advances. Searched terms included “mesothelioma,” “ESMO and NCCN guidelines,” “diagnosis,” “surgery,” “targeted therapy,” “clinical trials,” “palliative treatment,” and “meta-analysis.” First-line regimen recommendations have not evolved since the phase III pivotal study of cisplatin-pemetrexed was published, and this combination became the standard of care. Targeted therapies have brought disappointing results. However, recent clinical trial data with immunotherapies are bringing some light and may become a new paradigm in the following years.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73155",risUrl:"/chapter/ris/73155",book:{slug:"mesothelioma"},signatures:"Sonia Maciá",authors:[{id:"281982",title:"Dr.",name:"Sonia",middleName:null,surname:"Maciá",fullName:"Sonia Maciá",slug:"sonia-macia",email:"smacia@yahoo.com",position:null,institution:{name:"Miguel Hernandez University",institutionURL:null,country:{name:"Spain"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Epidemiology",level:"1"},{id:"sec_3",title:"3. Diagnosis",level:"1"},{id:"sec_4",title:"4. Pathology",level:"1"},{id:"sec_5",title:"5. Staging",level:"1"},{id:"sec_6",title:"6. Treatment for mesothelioma",level:"1"},{id:"sec_6_2",title:"6.1 First-line therapy for mesothelioma",level:"2"},{id:"sec_7_2",title:"6.2 Second-line therapy for mesothelioma",level:"2"},{id:"sec_8_2",title:"6.3 Immunotherapy and targeted therapies",level:"2"},{id:"sec_9_2",title:"6.4 Radiotherapy",level:"2"},{id:"sec_9_3",title:"6.4.1 Pre- and postoperative RT",level:"3"},{id:"sec_11_2",title:"6.5 Surgery",level:"2"},{id:"sec_11_3",title:"6.5.1 Surgery with radical intent",level:"3"},{id:"sec_12_3",title:"6.5.2 Surgery for staging and palliation",level:"3"},{id:"sec_15",title:"7. Conclusions",level:"1"},{id:"sec_19",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nBaas P, Fennell D, Kerr KM, Van Schil PE, Haas RL, Peters S. Malignant pleural mesothelioma: ESMO clinical practice guidelines. Annals of Oncology. 2015;26(Suppl 5):v31-v39\n'},{id:"B2",body:'\nAlpert N, van Gerwen M, Taioli E. Epidemiology of mesothelioma in the 21st century in Europe and the United States, 40 years after restricted/banned asbestos use. Transl Lung Cancer Res. 2020 Feb;9(Suppl 1):S28-S38\n'},{id:"B3",body:'\nEttinger D, Wood D. On behalf of nccn malignant pleural mesothelioma. NCCN Clinical Practice Guidelines in Oncology. 2019 November 27. https://www.nccn.org/professionals/physician_gls/pdf/mpm.pdf\n\n'},{id:"B4",body:'\nDi Noia V, Vita E, Ferrara M, et al. Malignant pleural mesothelioma: Is tailoring the second-line therapy really “raising the Bar?”. Current Treatment Options in Oncology. 2019;20(3):23. Published 2019 February 21. DOI: 10.1007/s11864-019-0616-7\n'},{id:"B5",body:'\nXu R, Barg FK, Emmet EA, et al. Association between mesothelioma and non-occupational asbestos exposure: Systematic review and meta-analysis. Environmental Health. 2018;17:90\n'},{id:"B6",body:'\nCarbone M, Kanodia S, Chao A, et al. Consensus report of the 2015 Weinman international conference on mesothelioma. Journal of Thoracic Oncology. 2016;11:1246-1262\n'},{id:"B7",body:'\nBetti M, Casalone E, Ferrante D, et al. Germline mutations in DNA repair genes predispose asbestos-exposed patients to malignant pleural mesothelioma. Cancer Letters. 2017;405:38-45\n'},{id:"B8",body:'\nYang H, Testa JR, Carbone M. Mesothelioma epidemiology, carcinogenesis and pathogenesis. Current Treatment Options in Oncology. 2008;9:147-157. DOI: 10.1007/s11864-008-0067-z\n'},{id:"B9",body:'\nPaintal A, Raparia K, Zakowski MF, Nayar R. The diagnosis of malignant mesothelioma in effusion citology: A reappraisal and results of a multi-institution survey. Cancer Cytopathology. 2013;121:703-707\n'},{id:"B10",body:'\nMaskell NA, Gleeson FV, Davies RJ. Standard pleural biopsy versus CT-guided cutting-needle biopsy for diagnosis of malignant disease in pleural effusions: A randomised controlled trial. Lancet. 2003;361:1326-1330\n'},{id:"B11",body:'\nGreillier L, Cavailles A, Fraticelli A, et al. Accuracy of pleural biopsy using thoracoscopy for the diagnosis of histologic subtype in patients with malignant pleural mesothelioma. Cancer. 2007;110:2248-2252\n'},{id:"B12",body:'\nChurg A, Roggli VL, Galateau-Salle F, et al. Tumours of the pleura: Mesothelial tumours. In: Travis WD, Brambilla E, Muller-Hermelink HK, Harris CC, editors. Pathology and Genetics of Tumours of the Lung, Pleura, Thymus and Heart. Lyon, France: IARC; 2004 (World Health Organization Classification of Tumours 10: 128-136)\n'},{id:"B13",body:'\nHusain AN, Colby T, Ordonez N, et al. Guidelines for pathologic diagnosis of malignant mesothelioma: 2012 update of the consensus statement from the International Mesothelioma Interest Group. Archives of Pathology & Laboratory Medicine. 2013;137:647-667\n'},{id:"B14",body:'\nRusch VW. A proposed new international TNM staging system for malignant pleural mesothelioma. From the International Mesothelioma Interest Group. Chest. 1995;108:1122-1128\n'},{id:"B15",body:'\nRice DC, Steliga MA, Stewart J, et al. Endoscopic ultrasound-guided fine needle aspiration for staging og malignant pleural mesothelioma. Ann Thorac Srug. 2009;88:862-868\n'},{id:"B16",body:'\nNowak AK, Armato SG III, Ceresoli GL, et al. Imaging in pleural mesothelioma: A review of imaging research presented at the 9th International Meeting of the International Mesothelioma Interest Group. Lung Cancer. 2010;70:1-6\n'},{id:"B17",body:'\nTammilehto L, Kivisaari L, Salminen US, et al. Evaluation of the clinical TNM staging system for malignant pleural mesothelioma: An assessment in 88 patients. Lung Cancer. 1995;12:25-34\n'},{id:"B18",body:'\nVogelzang NJ, Rusthoven JJ, Symanowski J, et al. Phase III study of pemetrexed in combination with cisplatin versus cisplatin alone in patients with malignant pleural mesothelioma. Journal of Clinical Oncology. 2003;21:2636-2644\n'},{id:"B19",body:'\nvan Meerbeeck JP, Gaafar R, Manegold C, et al. Randomized phase III study of cisplatin with or without raltitrexed in patients with malignant pleural mesothelioma: An intergroup study of the European Organisation for Research and Treatment of Cancer Lung Cancer Group and the National Cancer Institute of Canada. Journal of Clinical Oncology. 2005;23:6881-6889\n'},{id:"B20",body:'\nSantoro A, O’Brien ME, Stahel RA, et al. Pemetrexed plus cisplatin or pemetrexed plus carboplatin for chemonaive patients with malignant pleural mesothelioma: Results of the International Expanded Access Program. Journal of Thoracic Oncology. 2008;3:756-763\n'},{id:"B21",body:'\nCeresoli GL, Castagneto B, Zucali PA, et al. Pemetrexed plus carboplatin in elderly patients with malignant pleural mesothelioma: Combined analysis of two phase II trials. British Journal of Cancer. 2008;99:51-56\n'},{id:"B22",body:'\nKindler HL, Karrison TG, Gandara DR, et al. Multicenter, double-blind, placebo-controlled, randomized phase II trial of gemcitabine/cisplatin plus bevacizumab or placebo in patients with malignant mesothelioma. Journal of Clinical Oncology. 2012;30:2509-2515\n'},{id:"B23",body:'\nNowak AK, Millward MJ, Creaney J, et al. A phase II trial of intermittent sunitinib maleate as second-line therapy in progressive malignant pleural mesothelioma. Journal of Thoracic Oncology. 2012;7:1449-1456\n'},{id:"B24",body:'\nZalcman G, Mazieres J, Margery J, et al. Bevacizumab for newly diagnosed pleural mesothelioma in the mesothelioma Avastin Cisplatin Pemetrexed Study (MAPS): A randomized, controlled, open-label, phase 3 trial. Lancet. 2016;387:1405-1414\n'},{id:"B25",body:'\nManegold C, Symanowski J, Gatzemeier U, et al. Second-line (post-study) chemotherapy received by patients treated in the phase III trial of pemetrexed plus cisplatin versus cisplatin alone in malignant pleural mesothelioma. Annals of Oncology. 2005;16:923-927\n'},{id:"B26",body:'\nStebbing J, Powles T, McPherson K, et al. The efficacy and safety of weekly vinorelbine in relapsed malignant pleural mesothelioma. Lung Cancer. 2009;63:94-97\n'},{id:"B27",body:'\nHassan R, Miller AC, Sharon E, et al. Major cancer regressions in mesothelioma after treatment with an anti-mesothelin immunotoxin and immune suppression. Science Translational Medicine. 2013;5:208ra147\n'},{id:"B28",body:'\nCalabro L, Morra A, Fonsatti E, et al. Tremelimumab for patients with chemotherapy-resistant advanced malignant mesothelioma: An open-label, single-arm, phase 2 trial. The Lancet Oncology. 2013;14:1104-1111\n'},{id:"B29",body:'\nHom L, Spigel DR, Vokes EE, et al. Nivolumab versus docetaxel in previously treated patients with advanced non small cell lung cancer: Two year outcomes from two randomized, open label, phase III trials (checkmate 017 and checkmate 057). Journal of Clinical Oncology. 2017;35:3924-3933\n'},{id:"B30",body:'\nCalabro L, Morra A, Fonsatti E, et al. Efficacy and safety of an intensified schedule of tremelimumab for chemotherapy-resistant malignant mesothelioma: An open-label, singlearm, phase 2 study. The Lancet Respiratory Medicine. 2015;3:301-309\n'},{id:"B31",body:'\nMaio M, Scherpereel A, Calabro L, et al. Tremelimumab as second-line or third-line treatment in relapsed malignant mesothelioma (DETERMINE): A multicentre, international, randomised, double-blind, placebo-controlled phase 2b trial. The Lancet Oncology. 2017;18:1261-1273\n'},{id:"B32",body:'\nAlley EW, Lopez J, Santoro A, et al. Clinical safety and activity of pembrolizumab in patients with malignant pleural mesothelioma (KEYNOTE-028): Preliminary results from a non-randomised, open-label, phase 1b trial. The Lancet Oncology. 2017;18:623-630\n'},{id:"B33",body:'\nNamikawa K, Yamazaki N. Targeted therapy and immunotherapy for melanoma in Japan. Current Treatment Options in Oncology. 2019;20(1):7. Published 2019 January 24. DOI: 10.1007/s11864-019-0607-8\n'},{id:"B34",body:'\nQuispel-Janssen J, Zago G, Schouten R, et al. OA13.01 a phase II study of nivolumab in malignant pleural mesothelioma (NivoMes): With translational research (TR) biopies. Journal of Thoracic Oncology. 2017;12:S292-S293\n'},{id:"B35",body:'\nMacLeod N, Chalmers A, O’Rourke N, et al. Is radiotherapy useful for treating pain in mesothelioma? A phase II trial. Journal of Thoracic Oncology. 2015;10:944-950\n'},{id:"B36",body:'\nRusch VW, Rosenzweig K, Venkatraman E, et al. A phase II trial of surgical resection and adjuvant high-dose hemithoracic radiation for malignant pleural mesothelioma. The Journal of Thoracic and Cardiovascular Surgery. 2001;122:788-795\n'},{id:"B37",body:'\nAllen AM, Czerminska M, Jänne PA, et al. Fatal pneumonitis associated with intensity-modulated radiation therapy for mesothelioma. International Journal of Radiation Oncology, Biology, Physics. 2006;65:640-645\n'},{id:"B38",body:'\nRice D, Rusch V, Pass H, et al. Recommendations for uniform definitions of surgical techniques for malignant pleural mesothelioma: A consensus report of the international association for the study of lung cancer international staging committee and the international mesothelioma interest group. Journal of Thoracic Oncology. 2011;6:1304-1312\n'},{id:"B39",body:'\nRintoul RC, Ritchie AJ, Edwards JG, et al. Efficacy and cost of video-assisted thoracoscopic partial pleurectomy versus talc pleurodesis in patients with malignant pleural mesothelioma (MesoVATS): An open-label, randomised controlled trial. Lancet. 2014;384:1118-1127\n'},{id:"B40",body:'\nVan Schil PE, Baas P, Gafaar R, et al. Trimodality therapy for malignant pleural mesothelioma: Results from an EORTC phase II multicentre trial. Eur Resp J. 2010;36:1362-1369\n'},{id:"B41",body:'\nKrug LM, Pass HI, Rusch VW, et al. Multicenter phase II trial of neoadjuvant pemetrexed plus cisplatin followed by extrapleural pneumonectomy and radiation for malignant pleural mesothelioma. Journal of Clinical Oncology. 2009;27:3007-3013\n'},{id:"B42",body:'\nTreasure T, Lang-Lazdunski L, Waller D, et al. Extra-pleural pneumonectomy versus no extra-pleural pneumonectomy for patients with malignant pleural mesothelioma: Clinical outcomes of the mesothelioma and radical surgery (MARS) randomised feasibility study. The Lancet Oncology. 2011;12:763-772\n'},{id:"B43",body:'\nCao CQ , Yan TD, Bannon PG, McCaughan BC. A systematic review of extrapleural pneumonectomy for malignant pleural mesothelioma. Journal of Thoracic Oncology. 2010;5:1692-1703\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Sonia Maciá",address:"smacia@yahoo.com;, smacia@highlighttherapeutics.com",affiliation:'
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1. Introduction
In a surveillance camera without overlapping vision, a recognized object is identified again after imaging conditions (including monitoring scene, lighting conditions, object pose, etc.) change, which is called object re-identification (Object Re-ID). Object Re-ID technology has important research significance in intelligent monitoring, multi-object tracking and other fields. In recent years, scholars have paid extensive attention to it. The main application areas of object Re-ID are person Re-ID and vehicle Re-ID.
Person re-identification (Re-ID) is a technology that uses computer vision technology to judge whether there is a specific person in the image or video sequence. It is widely regarded as a sub-problem of image retrieval. Given a monitor person image, retrieve the image of the row of people across the device. It aims to make up for the visual limitations of the current fixed cameras, and can be combined with person detection and pedestrian tracking technology, which can be widely used in intelligent video monitoring, intelligent security and other fields.
Vehicle re-identification (Re-ID) aims to quickly search, locate and track the target vehicles across surveillance camera networks, which plays key roles in maintaining social public security and serves as a core module in the large-scale vehicle recognition, intelligent transportation, surveillance video analytic platforms. Vehicle Re-ID refers to the problem of identifying the same vehicle in a large scale vehicle database given a probe vehicle image. In particular, vehicle Re-ID can be regarded as a fine-grained recognition task that aims at recognizing the subordinate category of a given class. The wide popularization and use of road video monitoring makes vehicle matching based on video image become the hot spot in current intelligent traffic research, and the typical applications are vehicle origin-destination analysis and vehicle trajectory reconstruction. In some cases which license plate number could be recognized clearly and accurately, vehicle Re-ID could be realized by match the license plate number. However, in more cases, such as license plate can’t be recognized (for most surveillance video), license plate occlusion and so on in the criminal investigation, it is necessary to realize the vehicle Re-ID without license plates by using computer vision and other related technologies.
2. Related work of object Re-ID
As an emerging research topic, object Re-ID has attracted great efforts. Existing research directions of object Re-ID are mainly divided into person Re-ID and vehicle Re-ID. In this section, we will review the relevant works from person Re-ID and vehicle Re-ID.
2.1 Person Re-ID
We will review the relevant work [1] of person Re-ID from following aspects: person Re-ID based on representation learning, metric learning, local features and video sequence.
2.1.1 Person Re-ID based on representation learning
Methods based on representation learning are a kind of very common person Re-ID methods, which is mainly thanks to the deep learning, especially the Convolutional neural network (CNN) development. Sunderrajan et al. [2] propose a clothing context-aware color extraction method to learn color drift patterns in a non-parametric manner using the random forest distance (RFD) function. Geng et al. [3] proposed a person Re-ID algorithm which used Classification loss and verification loss to train the network (including Classification Subnet and Verification Subnet), and the network inputs several pairs of pedestrian images. The classification subnetwork makes ID prediction on the image, and calculates the classification error loss according to the predicted ID. The sub-network integrates the features of two images and judge whether these two images belong to the same pedestrian. The sub-network is essentially equivalent to a binary classification network. After enough data training, input a test image again, and the network will automatically extract a feature, which is used for person Re-ID. For the problem that pedestrian ID information alone is not enough to learn a model with strong generalization ability, the researchers added attributes such as gender, hair and clothing to the pedestrian images. By introducing the pedestrian attribute label, the model should not only accurately predict the pedestrian ID, but also predict the correct pedestrian attributes, which greatly increases the generalization ability of the model. Most papers also show that this method is effective. Lin et al. [4] proposed a person Re-ID algorithm based on multiple attributes. In this algorithm, the features of network output are not only used to predict the ID information of pedestrians, but also to predict the attributes of each pedestrian. The combination of ID loss and attribute loss can improve the generalization ability of the network. Currently, there is still a lot of work based on representational learning. Representational learning has also become a very important baseline of Re-ID field. Moreover, the method of representational learning is more robust, the training is more stable, and the results are easier to reproduce. However, representation learning is easy to be overfitted in the domain of the data set, and when the training ID is increased to a certain extent, it will be weak.
2.1.2 Person Re-ID based on metric learning
Metric learning is a method widely used in the field of image retrieval. Unlike representational learning, metric learning aims to learn the similarity between two images through the network. In the problem of person Re-ID, the similarity of different images of the same pedestrian is greater than that of different images of the different pedestrians. Finally, the loss function of the network makes the distance between the same pedestrian images (positive sample pairs) as small as possible, and the distance between different pedestrian images (negative sample pairs) as large as possible. Common measures of learning loss include Contrastive loss, Triplet loss, Quadruplet loss, Triplet hard loss with batch hard mining (TriHard loss) and Margin sample mining loss (MSML). Varior et al. [5] proposed Siamese Network, and trained the network model by contrast loss. By reducing the contrast loss, the distance between positive sample pairs is gradually reduced, and the distance between negative sample pairs is gradually increased, so as to meet the need of person Re-ID. Triplet loss is a widely used metric learning loss and a lot of metric learning methods have evolved based on triples. Ding et al. [6] considered the re-identification problem as a ranking issue and used triplet loss to obtain the relative distance between images. Chen et al. [7] designed a quadruplet loss process, which can lead to model outputs with larger inter-class variation and smaller intra-class variation compared with the triplet loss method. Hermans et al. [8] proposed a batch training based online difficult sample sampling method, which is named TriHard Loss. Traditional triplet sample mining strategy randomly select three images from training data, and most of the sampled images are simple and easily distinguishable sample pairs, which is not conducive to better representation of network learning. This paper proposes a sample mining strategy that can obtain more difficult samples which can improve the generalization ability of the network. Xiao et al. [9] proposed Margin sample mining loss which introduces the idea of hard sample sampling. MSML losses are calculated by picking only the hardest positive sample pair and the hardest negative sample pair. It is a measure learning method that takes into account both relative distance and absolute distance and introduces the idea of difficult sample sampling.
2.1.3 Person Re-ID based on local features
In the early stage of ReID’s research, people still focused on global feature, but later the global feature encountered a bottleneck, so they began to study local feature gradually. The commonly used methods to extract local features include image segmentation, positioning of skeleton key points and posture correction, etc. Image segmentation is a very common way to extract local features. Wei et al. [10] develop a pedestrian image descriptor named Global-Local-Alignment Descriptor, this descriptor explicitly leverages the local and global cues in human body to generate a discriminative and robust representation. In order to solve the failure of manual image slice in the case of image misalignment, some papers first align pedestrians with some prior knowledge, which mainly includes pre-trained human Pose and Skeleton key points model. Su et al. [11] proposed a pose-driven deep convolutional model to alleviate the pose variations and learn robust feature representations from both the global images and different local parts. Liang et al. [12] first estimated the key points of pedestrians with the model of attitude estimation, and then made the same key points align with affine transformation. To extract local features at different scales, they set three different PoseBox combinations; afterwards, the three PoseBox corrected images were sent to the network together with the original corrected images to extract features, which contained both global and local information. In order to solve the problem of local feature alignment, most methods need an additional skeleton key point or pose estimation model. Zhang et al. [13] proposed an automatic alignment model based on SP distance (AlignedReID), which automatically aligned local features without requiring additional information.
2.1.4 Person Re-ID based on video sequence
The main difference between video sequence-based methods is that such methods not only consider the content information of the image, but also consider the motion information between frames. Liu et al. [14] propose an algorithm called Accumulative motion context network (AMOC), the input of AMOC includes the original image sequence and the extracted optical flow sequence. AMOC has Spatial network and Motion network. Each frame of an image sequence is input into Spat Nets to extract the global content features of the image, the two adjacent frames will be sent to the Moti Nets to extract the optical flow pattern features; then the spatial features and optical flow features are merged and input into an RNN to extract the temporal features. Through the AMOC network, each image sequence can be extracted with a feature that integrates content information and motion information. The network adopts classification loss and comparison loss to train the model. Sequential image features with motion information can improve the accuracy of person Re-ID. Mazzeo et al. [15] propose a multi camera architecture for wide area surveillance and a real time people tracking algorithm across non overlapping cameras, they proposed different methodologies [16] to extract the color histogram information from each object patches for the intra-camera and compared different methods to evaluate the colour Brightness Transfer Function (BTF) between non overlapping cameras for inter-camera tracking. This method outperforms the performance in terms of matching rate between different cameras.
2.2 Vehicle Re-ID
We will review the relevant works of vehicle Re-ID from three aspects: vehicle re-identification based on artificial design feature, vehicle re-identification based on deep learning feature and vehicle re-identification based on fusion feature.
2.2.1 Vehicle Re-ID based on artificial design feature
In the initial vehicle matching problem, sensor tag matching is adopted. Tian et al. [17] proposed an algorithm for vehicle Re-ID based on multiple sensor nodes. According to the matching results of the same vehicle label obtained by different nodes, the vehicle state was determined and the label segmentation was modified. Meanwhile, the time difference between vehicles was modified according to the relationship between different acquired labels. Coifman [18] proposed a matching algorithm for individual vehicles measured on the highway detector and made corresponding measurements on another detector upstream. Rios-Cabrera et al. [19] proposed a comprehensive scheme for solving the problems of vehicle detection, recognition and tracking in view of the practical application of tunnel monitoring, and proposed compact binary features to improve the recognition effect for the influence of poor imaging conditions and vehicle lights in tunnel monitoring. Due to the late rise of vehicle Re-ID research, when traditional methods have not been applied to this problem too much, the deep learning technology has developed in a big bang. Almost all subsequent studies are based on deep learning technology, which greatly improves the effect of Re-ID.
2.2.2 Vehicle Re-ID based on deep learning feature
In recent years, convolutional neural network has been widely used in the field of computer vision and achieved remarkable effects. Because the depth features extracted by deep convolutional networks have stronger description ability, more and more scholars have applied them in vehicle Re-ID. Liu et al. [20] proposed a large-scale vehicle Re-ID data set “VeRi,” and puts forward a method of feature Fusion FACT by combining the depth of the vehicle network features, color features and SIFT features to match the same vehicle, the follow-up of vehicle recognition of other study, a large number of experiment based on the data set, thereby evaluating effectiveness and superiority of the proposed algorithm. Liu et al. [21] solved the problem of difficulty in triplet loss convergence by adding a feature representation between the sample and each individual vehicle into the triplet network to model intra-class variance. Li et al. [22] proposed DJDL (Deep Joint Discriminative Learning) model, which projects the original vehicle image into Euclidean space through a two-branch Deep convolution network. Zhang et al. [23] proposed a guided Triplet network, which added classification loss to the original triplet loss function and strongly restricted the original training network, thus improving the Re-ID efficiency. Marin et al. [24] designed a metric learning model based on the supervision of the local constraints, its use in pairs and triple constraints to train a network, the network is able to share the same identity of the sample distribution of high similarity, and keep a distance of different identity in the feature space, the algorithm is one of the biggest advantage is to use the vehicle tracking to automatically generate a set of weak tag data, and will automatically generate data sets used in depth training network to complete the vehicles Re-ID task.
2.2.3 Vehicle Re-ID based on fusion feature
For monitoring video, in addition to appearance information of images, information other than appearance features (such as, space-time information) is also of great mining significance. Liu et al. [25] proposed a segmented vehicle Re-ID algorithm, which first used appearance features for preliminary screening, then used license plate information for matching, and finally used spatial and temporal information for reordering. After the method was integrated with spatial and temporal information, the effect was improved to a certain extent. Jiang et al. [26] proposed a vehicle Re-ID algorithm based on multiple attribute training and sort by spatial-temporal similarity, the vehicle image color, models, vehicle feature extraction with individual respectively. Through the fusion of multiple features for the initial Re-ID, the Re-ID results are reordered by the spatial-temporal similarity, and good results are obtained. Shen et al. [27] proposed a two-stage architecture containing complex spatiotemporal information, given a pair of vehicle images with spatio-temporal information, candidate visual spatio-temporal paths (where each visual spatio-temporal state corresponds to an actual vehicle image with spatio-temporal information) are generated by an MRF chain model with a deep learning function, and then candidate paths and paired queries are used to generate their similarity scores for the model. In addition to fusion of information other than the apparent features of images, many scholars have also studied fusion of manual features and deep convolution features, fusion of various attribute features or feature fusion between different image regions. Li et al. [28] proposed a vehicle Re-ID algorithm based on fusion features extract from different part of vehicle, firstly, a part detection algorithm [29] is used to obtain the attention area with big difference between different vehicles. Then, feature extraction was carried out on the detected area, and features of the two areas were fused to generate new fusion features. Liang et al. [30] put forward a new method of supervision and the depth of the hash to handle large-scale vehicle search problem, the use of multitasking learning to learn, vehicle model, vehicle image color depth features of individual ID hash code, the experimental results show the effectiveness of the proposed method, the method in classification loss and triple loss case depth hash method is superior to single task.
3. Some public database for object Re-ID
With the development of Re-ID research, many scholars have published the data sets of relevant fields. The following are some commonly used person Re-ID data sets and vehicle Re-ID data sets.
3.1 Person Re-ID data sets
Person Re-ID data sets commonly used in deep learning methods include VIPeR [31], PRID2011 [32], CUHK03 [33], Market1501 [34], CUHK-SYSU [35], MARS [36], DukeMTMC-reID [37]. In addition to the common data sets that are already open source, there are several newer data sets, such as SYSU-MM01 [38], LPW [39], MSMT17 [40], LVreID [41], the download link is not yet open. The following is a detailed description of CUHK03 and Market1501.
3.1.1 CUHK03
The dataset includes 13,164 images of 1360 pedestrians. The whole dataset is captured with six surveillance cameras. Each identity is observed by two disjoint camera views and has an average of 4–8 images in each view. Some examples are shown in Figure 1. Besides the scale, it has the following characteristics.
Figure 1.
Person samples selected from the CUHK03 dataset.
This dataset is partitioned into training set (1160 persons), validation set (100 persons), and test set (100 persons). Each person has roughly 4–8 photos per view, which means there are almost 26,000 positive training pairs before data augmentation.
3.1.2 Market1501
During dataset collection, a total of six cameras were placed in front of a campus supermarket, including five 1280 × 1080 HD cameras, and one 720 × 576 SD camera. Overlapping exists among these cameras. This dataset contains 32,668 boxes of 1501 identities. Due to the open environment, images of each identity are captured by at most six cameras. Each annotated identity is captured by at least two cameras, so that cross-camera search can be performed. Overall, the dataset has the following featured properties.
The dataset is randomly divided into training and testing sets, containing 750 and 751 identities, respectively. During testing, for each identity, it selects one query image in each camera. Note that, the selected queries are hand-drawn, instead of DPM-detected as in the gallery. The reason is that in reality, it is very convenient to interactively draw a box, which can yield higher recognition accuracy. The search process is performed in a cross-camera mode, i.e., relevant images captured in the same camera as the query are viewed as “junk.” In this scenario, an identity has at most six queries, and there are 3368 query images in total. Dataset examples are shown in Figure 2.
Figure 2.
Person samples selected from the Market1501 dataset.
3.2 Vehicle Re-ID data sets
Vehicle Re-ID data sets commonly used in deep learning methods include VRID-1 [42], VeRi-776 [25], VehicleID [21].
3.2.1 VRID-1
The open dataset VRID-1 for vehicle re-identification contains 10,000 images, which are captured by 326 surveillance cameras within 14 days. The resolutions of images are distributed from 400 × 424 to 990 × 1134. VRID collects 1000 vehicle IDs (vehicle identities) of top 10 common vehicle models (Table 1) to reconstruct the interference with the same vehicle model in the real world. The vehicle IDs belong to the same model have very similar appearance and their differences appears in the area of the logo and accessories. Besides, each vehicle IDs contains 10 images which are in various illuminations, poses and weather condition. Dataset examples are shown in Figure 3.
Vehicle model
Vehicle IDs
Total images
Audi_A4
100
1000
Honda_Accord
100
1000
Buick_Lacrosse
100
1000
Volkswagen_Magotan
100
1000
Toyota_Corolla_I
100
1000
Toyota_Corolla_II
100
1000
Toyota_Camry
100
1000
Ford_Focus
100
1000
Nissan_Tiida
100
1000
Nissan_Sylphy
100
1000
Table 1.
The 10 vehicle models in the dataset.
Figure 3.
Vehicle samples selected from the VRID-1 dataset.
The attributes of VRID is illustrated in Table 2. The vehicle model column represents the vehicle model information. The license plate column is used for the correlation of the same vehicle. The window location column shows the location of vehicle window area. The vehicle color column contains the vehicle color information. Besides, with the rich attributes of vehicles, the dataset could also be used for vehicle fine-grained recognition as well as vehicle color recognition.
Image_ID
Vehicle model
License plant number
Window location
Color
IDs_1
Toyota_Corolla
License_1
X1, Y1, X2, Y2
Yellow
IDs_12
Toyota_Corolla
License_2
X1, Y1, X2, Y2
Black
IDs_1000
Honda_Accord
License_10
X1, Y1, X2, Y2
White
Table 2.
The attributes of VRID.
3.2.2 VeRi-776
To collect high-quality videos in real-world surveillance scene, we select 20 cameras deployed along a circular road of a 1.0 km2 area as shown in Figure 4. The scenes of the cameras include two-lane roads, four-lane roads, and crossroads. All cameras are set to 1920 × 1080 resolution and 25 fps. The cameras are deployed with arbitrary orientations and tilt angles. Besides, there are overlaps for part of the cameras.
Figure 4.
The urban surveillance environments and cameras distribution for the VeRi dataset.
The VeRi dataset is collected with 20 cameras in real-world traffic surveillance environment. A total of 776 vehicles are annotated. Two hundred vehicles are used for testing. The remaining 576 vehicles are for testing. There are 11,579 images in the test set, 1678 images as queries and 37,778 images in the training set. Each vehicle is captured by at least two cameras. One advantage of this data set is that the camera ID and timestamp (frame ID) are reserved with tracks for further annotation. Dataset examples are shown in Figure 5.
Figure 5.
Vehicle samples selected from the VeRi dataset.
4. General technical route
In deep learning method, the general technical route of object Re-ID includes three stages: data input stage, feature extraction model and distance measurement (Figure 6).
Figure 6.
General technical route of object re-identification.
4.1 Data input
Data input mainly refers to feeding data to feature extraction model, and the commonly used data type in object Re-ID is three-channel image. In this part, we do not describe the input data, but mainly introduce data augmentation. In the training stage of deep learning model, insufficient data often leads to the situation that the model cannot converge or overfit. In order to avoid this situation, data augmentation is one of the solutions. Common operations for data augmentation are as follows:
Color Jittering. Color data enhancement, such as Change image brightness, saturation, contrast and so on.
Random Scale. Randomly change the original size of the image.
Horizontal/vertical flip. Flip the original image horizontally or vertically.
In the data input stage, we need to pay attention to not only the data amplification, but also, in some special cases such as contrast loss or triplet loss model, we may need to construct the image pair or triplet sample in advance. Due to the limitation of GPU memory, it is impossible to input a batch of data includes all images, so it is possible that there is no negative sample which might result in the failure of image pair or triplet sample construction, at the same time, due to the large number of target individuals in the re-identification problem, the imbalance between positive and negative samples is very likely to exist, which easily leads to the unscientific network model trained. Therefore, we need to set some rules in the data input stage to correctly construct these image pairs or triplet samples.
4.2 Feature extraction model
The core of object Re-ID algorithm is feature extraction model, the effectiveness of the whole algorithm is also almost determined by this part. In other words, the essence of Re-ID is to compare the similarity or distance between the features extracted from two images. Image features mainly include color feature, texture feature, shape feature and spatial relationship feature. Feature extraction is a concept in computer vision and image processing. It refers to the use of computer to extract image information to determine whether each image pixel belongs to an image feature. Features are the best way to describe patterns, and we often think that each dimension of a feature can describe a pattern from a different perspective. Ideally, the dimensions are complementary and complete. In the field of image recognition or image Re-ID, traditional methods of feature extraction include Histogram of Oriented Gradient (HOG), scale-invariant feature transform (SIFT), Speeded Up Robust Features (SURF), Local Binary Pattern (LBP) and so on; the deep learning methods of feature extraction include Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and so on. We present a feature extraction method in detail in both traditional and deep learning methods.
4.2.1 Histogram of oriented gradient (HOG)
The essence of HOG feature extraction is to constitute features by computing and statistics the histogram of gradient direction in the local area of the image. Hog feature combined with SVM classifier has been widely used in image recognition, especially in pedestrian detection, which has achieved great success. How to extract HOG feature? Firstly, the image is divided into small connected regions, which are called cell units. Then the direction histogram of the gradient or edge of each pixel in the cell is collected. Finally, these histograms can be combined to form a feature descriptor.
4.2.2 Convolution neural network (CNN)
It is a kind of feedforward neural network with deep structure including convolution calculation. A convolutional neural network contains three types of neural network layers: convolutional layer, pooling layer and fully connection layer. As is shown in Figure 7.
Figure 7.
Basic structure diagram of convolutional neural network.
4.2.2.1 Convolutional layer
The convolution layer is mainly used for learning the feature representation of input data. The convolution layer is composed of multiple convolution kernels, and the convolution operation is carried out on the input image to calculate different feature maps.
In general, the input data is RGB image, as shown in Figure 8. If the color image is 6*6*3, the three refers to three color channels, and the convolution operation is carried out with a 3*3*3 convolution kernel, corresponding to the red, green and blue channels. Take the 27 numbers in turn, multiply them by the Numbers in the corresponding red, green and blue channel, and then add them all up to get the first number in the output of the feature graph.
Figure 8.
Convolution diagram.
The convolution layer principle is shown in equation:
xjl=f∑ixil−1∗kijl+bjlE1
where f∗ is activation functions; xjl denotes the j−th feature map of output layer l, xil is the i−th feature map of the layer l; Kijl represents the convolution kernel of the i−th feature graph of the current input layer and the j−th feature graph of the output layer on the layer l; bjl is the bias term of the j−th feature graph in the layer l.
4.2.2.2 Pooling layer
Pooling layer is often used in the convolutional network to reduce the size of the model, improve the computational speed, and improve the robustness of extracted features. Pooling operation can maintain the invariance of translation, rotation and scale. Common pooling layer operations are averaging and pooling. The maximum pooling operation is as shown in Figure 9. The input of 4*4 is divided into different regions. For the output of 2*2, each element output is the maximum element value in its corresponding color region.
Figure 9.
Max pooling diagram.
4.2.2.3 Fully connection layer
Each node of the fully connection layer is connected to all nodes of the previous layer to integrate the features extracted from the previous layer. Due to its fully connected nature, the general fully connected layer also has the most parameters. The full join layer act as a mapping of the learned “distributed feature representation” into the sample tag space. It’s essentially a linear transformation from one eigenspace to another eigenspace. Any dimension of the target space is affected by every dimension of the source space. In CNN, the full connection is often found in the last few layers, which is used to make a weighted sum of the features designed before. The schematic diagram of the entire connection layer is shown in Figure 10.
Figure 10.
Fully connection layer.
4.3 Distance measurement
After feature extraction, we need to compare the distance between the query image and all images in the retrieval set, and there are many ways you can measure the difference between two features, It is divided into distance measure (such as, Euclidean distance, Manhattan Distance etc.) and similarity measure (such as, Cosine Similarity, Jaccard Coefficient, etc.).
Distance measure is used to measure the distance of an individual in space, the greater the distance, the greater the difference between individuals. Similarity measurement is to calculate the degree of similarity between individuals. Contrary to distance measurement, the smaller the value of similarity measurement is, the smaller the similarity between individuals is, and the greater the difference is. Therefore, we can judge which images are more likely to be the same individual by the value of the difference between image features.
5. One case of person Re-ID
Person Re-ID is a technology that uses computer vision technology to judge whether there is a specific person in the image or video sequence. It is widely regarded as a sub-problem of image retrieval. Given a monitor person image, retrieve the image of the row of people across the device. It aims to make up for the visual limitations of the current fixed cameras, and can be combined with person detection and pedestrian tracking technology, which can be widely used in intelligent video monitoring, intelligent security and other fields. In this section, we show a classic person Re-ID algorithm Part-based Convolutional Baseline (PCB) [43].
5.1 Structure of PCB
PCB can take any network without hidden fully-connected layers designed for image classification as the backbone, e.g., Google Inception and ResNet. Original paper employs ResNet50 as the backbone network to reproduce the PCB algorithm.
The structure of PCB illustrated in Figure 11. The input image goes forward through the stacked convolutional layers from the backbone network to form a 3D tensor T. PCB replaces the original global pooling layer with a conventional pooling layer, to spatially down-sample T into p pieces of column vectors g. A following 1 × 1 kernel-sized convolutional layer reduces the dimension of g. Finally, each dimension-reduced column vector h is input into a classifier, respectively. Each classifier is implemented with a fully-connected (FC) layer and a sequential Softmax layer. During training, each classifier predicts the identity of the input image and is supervised by Cross-Entropy loss. During testing, either p pieces of g or h are concatenated to form the final descriptor of the input image.
Figure 11.
Structure of PCB [43].
5.2 Experimental results
5.2.1 Dataset
The original paper tested this algorithm on person Re-ID dataset Market-1501. The Market-1501 dataset contains 1501 identities observed under six camera viewpoints, 19,732 gallery images and 12,936 training images detected by DPM.
5.2.2 Performance comparison
It compares PCB and PCB + RPP with state of the art. Comparisons on Market-1501 are detailed in Table 3. PCB + RPP get mAP = 81.6% and Rank-1 = 93.8% for Market-1501, setting new state of the art on this dataset. All the results are achieved under the single-query mode without re-ranking. Reranking methods will further boost the performance especially mAP. For example, when “PCB + RPP” is combined with the reranking method, mAP and Rank-1 accuracy on Market-1501 increases to 91.9 and 95.1%, respectively.
Methods
Rank-1
Rank-5
Rank-10
mAP
KLFDA
46.5
71.1
79.9
—
Triplet Loss
84.9
94.2
—
69.1
DML
87.7
—
—
68.8
MultiScale
88.9
—
—
73.1
GLAD
89.9
—
—
73.9
PCB
92.3
97.2
98.2
77.4
PCB + RPP
93.8
97.5
98.5
81.6
Table 3.
Comparison of the proposed method with the art on Market-1501.
6. One case of vehicle Re-ID
Considering the spatiotemporal logic of vehicle driving process, we present a vehicle re-identification (Re-ID) algorithm based on multi-camera data’s spatiotemporal information and joint learning mechanism without license plate. The algorithm is divided into feature extraction and spatiotemporal re-rank. In the feature extraction stage: on the basis of convolutional neural network (CNN), triplet loss and Softmax loss were used for joint training to model a feature extractor and calculate the feature distance measurement matrix between query image and retrieval set images. In the spatiotemporal re-rank stage: we calculate the spatiotemporal distance matrix and fuse the spatiotemporal distance with the normalized feature distance metric. The final distance measurement matrix is sorted to obtain the vehicle re-identification result. Extensive experiments were carried out on the benchmark datasets “VeRi” to verify the effectiveness of the proposed method and the result have shown that the proposed algorithm outperforms the state-of-the-art approaches for vehicle Re-ID.
6.1 Mathematical principles of joint learning
The architecture of proposed algorithm is illustrated in Figure 12. The algorithm is divided into two steps: feature extraction and spatiotemporal re-rank. In the feature extraction phase, triplet loss and Softmax loss are integrated for joint training, triplet loss is used to calculate the distance of the sample features, increasing the distance between the anchor and negative sample, reducing the distance between the anchor and the positive sample. Softmax loss performs label-level supervision and constraint on the feature extraction network. In the spatiotemporal re-rank stage, calculating the spatiotemporal distance between images, and re-rank the retrieval results by merging the spatiotemporal distance and the feature distance.
Figure 12.
The proposed algorithm for vehicle Re-ID.
6.1.1 Triplet loss
In order to learn high discriminative features from images to Euclidean space, where the distance can measure the discrepancy between two images. The idea of learning to rank has gradually been applied to many fields, such as face recognition [12], person Re-ID, and so on. One of the important steps in learning to rank is to find a good similarity function, and triplet loss is a very broad one. In the calculation of the triple loss, the feed data includes an anchor, a positive sample, and a negative sample, and the sample similarity calculation is realized by optimizing the distance between the anchor and the positive sample being smaller than the distance between the anchor and the negative sample. We suppose T=xii=12…m denotes the training set, where xi is the i−th image in the training set and m denotes the total amount of training images. For an image triplet xiaxipxin, where xia denotes an anchor, xip denotes a positive of the same class as the anchor, xin denotes a negative of a different class as the anchor, the triplet loss is calculated as Eq. (2).
Ltriplet=∑immax0fxia−fxip22−fxia−fxin22+αE2
where fxi denotes the embedded of the image, α denotes the parameter of expected gap between the distance of xiaxip and xiaxin
6.1.2 Triplet sampling
This algorithm directly performs on-line triplet mining on image features, which is to compute useful triplets on the fly. For each batch of inputs, given a batch of N examples, we compute the N embeddings and we then can find a maximum of N3 triplets. For three indices a, p, n∈[1, N], if examples a and p have the same label but are distinct, and example n has a different label, we say that (a, p, n) is a valid triplet. We suppose that have a batch of vehicle images as input of size N = PK, composed of P different vehicle ID with K images each. Choose the batch hard strategy: for each anchor, select the hardest positive and the hardest negative among the batch, finally we can obtain PK triplets.
dab=a−b2=a2−2ap+b2E3
6.1.3 Softmax loss
We impose a strong constraint on distinguishing different vehicle label by adding Softmax loss to the loss function. The embedded obtained by CNN tend to clusters, and the embedded of same vehicle ID will be similar, so the convergence time of triplet loss will be cut down. In Softmax loss stream, each vehicle ID in the training set is considered as a category, the Softmax loss function is formulated as:
Lsoftmax=−1m∑i=1m∑j=1k1yi=jlogeθjTxi∑l=1keθlTxiE4
where m is the total amount of classes, k is the number of training image, 1∗ is the indicator function (if * is true, then the value set 1, or 0), and θ\'s are the parameters of the final full-connection layer of the CNN.
6.1.4 Joint multiple loss
The joint learning mechanism is mainly applied to the training phase of vehicle images. After the shared images pass through the shared CNN layer, they are divided into two branch streams, one is subjected to online triplet mining for the calculation of triplet loss, and the other stream enters the Softmax layer for Softmax loss calculation. The final joint learning loss function can be formulated as:
LJL=Lsoftmax+LtripletE5
6.2 Experimental results
6.2.1 Dataset
In order to verify the validity of the algorithm, we conduct experiments in the latest version of the vehicle re-identification dataset “VeRi.” The dataset has a total of 49,357 images, which are taken for actual road monitoring and contains various angles and various vehicle models, as shown in Figure 13.
Figure 13.
Vehicle samples selected from the VeRi dataset.
It is divided into two subsets for training and testing. The train set has 576 vehicle IDs with 37,778 images and the test set has 200 vehicle IDs with 11,579 images. For the vehicle re-identification task, we divided the test set to query set (1678 images) and retrieval set (9901 images).
6.2.2 Experimental setting
All of the experiments are based on the deep learning framework Tensorflow. The base network is VGG_CNN_M, and the model was pre-trained on the ImageNet. In the calculation of triplet loss, we set α=1, the learning rate is set to 0.001, and the mini-batch is set to 32.
In order to evaluate the effect of this algorithm objectively, we set up two algorithms to compare with the method proposed to verify that the improvement of the algorithm. These algorithms are: (1) VGG + Softmax loss; (2) VGG + Triplet loss; (3) VGG + Softmax loss + Triplet loss (our method). All of network based on VGG16, “Softmax loss” denotes use Softmax loss to train the network, and “Triplet loss” denotes use triplet loss to train the network. At the same time, we also make comparison our experiment results with some state-of-the-art algorithms on the same dataset “VeRi.”
6.2.3 Performance comparison on VeRi dataset
We conduct the experiment as described in experimental setting, and use cumulative matching curve (CMC), HIT@1, HIT@5 as metrics to evaluate the performance. In our method, S, T denote using Softmax loss and using triplet loss respectively. Table 4 and Figure 14 illustrate the performances of the proposed methods and some state-of-the-art algorithms in vehicle Re-ID field.
Method
HIT@1
HIT@5
BOW-CN
33.91
53.69
LOMO
25.33
46.48
ABLN
58.14
74.41
FACT + Plate-SNN+ STR
61.44
78.78
VAMI
77.03
90.92
JFSDL
82.90
91.60
This method
VGG+ S
72.94
83.67
VGG + T
72.94
86.83
VGG + S + T
89.75
95.05
Table 4.
Comparison of the proposed method with the art on VeRi.
Figure 14.
The CMC curves on VeRi.
The results show that the proposed method “VGG + S + T” achieves the best results, the HIT@1 and HIT@5 hit 89.75 and 95.05% respectively. It is obvious that the CNN-based method has a significant improvement over the handcraft feature-based approach when compare BOW-CN and LOMO algorithm with other algorithms based on CNN feature. Compared with “VGG + S” which only utilizes Softmax loss, our method has much better results, improving 16.81% in HIT@1 and 11.38% in HIT@5. Compared with “VGG + T” which only utilizes triplet loss, our method makes improvement about 16.81% in HIT@1 and 8.67% in HIT@5. Compare to “FACT + Plate-SNN + STR” which additionally utilizes license plate information (Plate-SNN) and spatiotemporal relation (STR), our method improves 28.31% in HIT@1 and 16.27% in HIT@5. In summary, the proposed algorithm is feasible in vehicle re-identification task, and achieves outstanding results compared to other algorithms.
7. Summary
This chapter mainly introduces the concept of object Re-ID and two core applications: person Re-ID and vehicle Re-ID. In this chapter, the definitions of person Re-ID and vehicle Re-ID are given, some research methods of the two applications are reviewed, and the commonly used public data sets are described in detail.
In this chapter, the general process of object Re-ID by deep learning method is given, and the data input, feature extraction network structure, distance measurement and other parts are described in detail. At the same time, two examples are given to illustrate the algorithm in detail and experiment comparison. Person Re-ID refers to the network structure and experimental results of PCB algorithm [43]. Vehicle Re-ID is introduced in detail in terms of feature extraction and measurement calculation. The influence of parameters in the deep learning method is illustrated through the analysis of experimental results, and the evaluation comparison is given.
These can help relevant researchers to understand the context of technology, the general implementation process, as well as important parameters and evaluation indicators in this field, so that they can quickly start relevant research.
The object Re-ID is the basis of realizing cross-camera tracking. Person and vehicles are just two typical applications. In the future, with the gradual solution of the following problems, we will have a more extensive application:
High-quality standard database is important to generalization performance of Re-ID algorithm. The database should be more suitable for the real environment and including different and varying scenes.
Deep networks have poor interpretability. Although the deep learning method has achieved good performance in Re-ID tasks, few studies have shown which information has a greater impact on Re-ID behind the continuous improvement in accuracy.
At present, most methods are carried out under the prior condition that object has been detected, but this requires a very robust detection model. We need to combine object Re-ID with object detection, which is more in line with practical application requirements.
The research should focus on semi-supervised, unsupervised and transfer learning methods. The collected data are limited after all, and the cost of labeling data is also very high. Therefore, although the semi-supervised and unsupervised learning methods may not be as good as the supervised learning methods in terms of performance, they are valuable.
Acknowledgments
This work is supported by the National key Research and Development Program of China under Grant No. 2018YFB1601101 of 2018YFB1601100 and National Natural Science Foundation of China under Grant No. U1611461.
\n',keywords:"object re-identification, deep learning, person re-identification, vehicle re-identification, feature extraction",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67472.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67472.xml",downloadPdfUrl:"/chapter/pdf-download/67472",previewPdfUrl:"/chapter/pdf-preview/67472",totalDownloads:802,totalViews:0,totalCrossrefCites:0,dateSubmitted:"February 1st 2019",dateReviewed:"April 27th 2019",datePrePublished:"June 23rd 2019",datePublished:null,dateFinished:null,readingETA:"0",abstract:"With the explosive growth of video data and the rapid development of computer vision technology, more and more relevant technologies are applied in our real life, one of which is object re-identification (Re-ID) technology. Object Re-ID is currently concentrated in the field of person Re-ID and vehicle Re-ID, which is mainly used to realize the cross-vision tracking of person/vehicle and trajectory prediction. This chapter combines theory and practice to explain why the deep network can re-identify the object. To introduce the main technical route of object Re-ID, the examples of person/vehicle Re-ID are given, and the improvement points of existing object Re-ID research are described separately.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/67472",risUrl:"/chapter/ris/67472",signatures:"Xiying Li and Zhihao Zhou",book:{id:"8725",title:"Visual Object Tracking with Deep Neural Networks",subtitle:null,fullTitle:"Visual Object Tracking with Deep Neural Networks",slug:"visual-object-tracking-with-deep-neural-networks",publishedDate:"December 18th 2019",bookSignature:"Pier Luigi Mazzeo, Srinivasan Ramakrishnan and Paolo Spagnolo",coverURL:"https://cdn.intechopen.com/books/images_new/8725.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"17191",title:"Dr.",name:"Pier Luigi",middleName:null,surname:"Mazzeo",slug:"pier-luigi-mazzeo",fullName:"Pier Luigi Mazzeo"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related work of object Re-ID",level:"1"},{id:"sec_2_2",title:"2.1 Person Re-ID",level:"2"},{id:"sec_2_3",title:"2.1.1 Person Re-ID based on representation learning",level:"3"},{id:"sec_3_3",title:"2.1.2 Person Re-ID based on metric learning",level:"3"},{id:"sec_4_3",title:"2.1.3 Person Re-ID based on local features",level:"3"},{id:"sec_5_3",title:"2.1.4 Person Re-ID based on video sequence",level:"3"},{id:"sec_7_2",title:"2.2 Vehicle Re-ID",level:"2"},{id:"sec_7_3",title:"2.2.1 Vehicle Re-ID based on artificial design feature",level:"3"},{id:"sec_8_3",title:"2.2.2 Vehicle Re-ID based on deep learning feature",level:"3"},{id:"sec_9_3",title:"2.2.3 Vehicle Re-ID based on fusion feature",level:"3"},{id:"sec_12",title:"3. Some public database for object Re-ID",level:"1"},{id:"sec_12_2",title:"3.1 Person Re-ID data sets",level:"2"},{id:"sec_12_3",title:"3.1.1 CUHK03",level:"3"},{id:"sec_13_3",title:"3.1.2 Market1501",level:"3"},{id:"sec_15_2",title:"3.2 Vehicle Re-ID data sets",level:"2"},{id:"sec_15_3",title:"Table 1.",level:"3"},{id:"sec_16_3",title:"3.2.2 VeRi-776",level:"3"},{id:"sec_19",title:"4. General technical route",level:"1"},{id:"sec_19_2",title:"4.1 Data input",level:"2"},{id:"sec_20_2",title:"4.2 Feature extraction model",level:"2"},{id:"sec_20_3",title:"4.2.1 Histogram of oriented gradient (HOG)",level:"3"},{id:"sec_21_3",title:"4.2.2 Convolution neural network (CNN)",level:"3"},{id:"sec_21_4",title:"4.2.2.1 Convolutional layer",level:"4"},{id:"sec_22_4",title:"4.2.2.2 Pooling layer",level:"4"},{id:"sec_23_4",title:"4.2.2.3 Fully connection layer",level:"4"},{id:"sec_26_2",title:"4.3 Distance measurement",level:"2"},{id:"sec_28",title:"5. One case of person Re-ID",level:"1"},{id:"sec_28_2",title:"5.1 Structure of PCB",level:"2"},{id:"sec_29_2",title:"5.2 Experimental results",level:"2"},{id:"sec_29_3",title:"5.2.1 Dataset",level:"3"},{id:"sec_30_3",title:"Table 3.",level:"3"},{id:"sec_33",title:"6. One case of vehicle Re-ID",level:"1"},{id:"sec_33_2",title:"6.1 Mathematical principles of joint learning",level:"2"},{id:"sec_33_3",title:"6.1.1 Triplet loss",level:"3"},{id:"sec_34_3",title:"6.1.2 Triplet sampling",level:"3"},{id:"sec_35_3",title:"6.1.3 Softmax loss",level:"3"},{id:"sec_36_3",title:"6.1.4 Joint multiple loss",level:"3"},{id:"sec_38_2",title:"6.2 Experimental results",level:"2"},{id:"sec_38_3",title:"6.2.1 Dataset",level:"3"},{id:"sec_39_3",title:"6.2.2 Experimental setting",level:"3"},{id:"sec_40_3",title:"Table 4.",level:"3"},{id:"sec_43",title:"7. Summary",level:"1"},{id:"sec_44",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Luo H, Wei J, Xing F, Si-Peng Z. 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School of Intelligent Systems Engineering, Sun Yat-sen University, People’s Republic of China
Guangdong Provincial Key Laboratory of Intelligent Transportation System, People’s Republic of China
Laboratory of Video and Image Intelligent Analysis and Application Technology, Ministry of Public Security, People’s Republic of China
School of Intelligent Systems Engineering, Sun Yat-sen University, People’s Republic of China
Guangdong Provincial Key Laboratory of Intelligent Transportation System, People’s Republic of China
Laboratory of Video and Image Intelligent Analysis and Application Technology, Ministry of Public Security, People’s Republic of China
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He received a Medical Doctor degree by the Universidad Nacional Autonoma de Mexico (UNAM). Afterwards he obtained MSc Sociomedical in the area of Epidemiology and in 2004 his PhD degree, with thesis: “Molecular epidemiology of childhood acute leukemias. An assessment of a causal model, interaction of three factors: susceptibility, exposition and vulnerable time”, for which he obtained Acknowledgement of Merit. Dr. Mejia-Arangure is member of Mexican and international societies including: Sistema Nacional de Investigadores, Academia Mexicana de Pediatria, Agrupacion Mexicana para el Estudio de la Hematologia, American Society of Hematology, the Society of Epidemiologic Research and the International Society for Environmental Epidemiology. He wrote 71 scientific articles, 13 book chapters and the present is his second book as Editor.",institutionString:null,institution:{name:"Mexican Social Security Institute",institutionURL:null,country:{name:"Mexico"}}},{id:"34180",title:"Prof.",name:"Francisco",surname:"Ascaso",slug:"francisco-ascaso",fullName:"Francisco Ascaso",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"45515",title:"Prof.",name:"Arturo",surname:"Fajardo-Gutierrez",slug:"arturo-fajardo-gutierrez",fullName:"Arturo Fajardo-Gutierrez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"45516",title:"MSc.",name:"Maria Luisa",surname:"Perez-Saldivar",slug:"maria-luisa-perez-saldivar",fullName:"Maria Luisa Perez-Saldivar",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"65478",title:"Dr",name:"Shoko",surname:"Kobayashi",slug:"shoko-kobayashi",fullName:"Shoko Kobayashi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"74711",title:"Dr.",name:"Rosana",surname:"Pelayo",slug:"rosana-pelayo",fullName:"Rosana Pelayo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/74711/images/3659_n.jpg",biography:"Rosana Pelayo obtained her Bachelors, Masters and Doctorate degrees from the National Autonomous University of Mexico and completed her postdoctoral fellowship at the Oklahoma Medical Research Foundation, in the Immunobiology and Cancer Program. Dr Pelayo is currently Senior Research Scientist and Principal Investigator in the Oncology Research Unit of the Mexican Institute for Social Security, in Mexico City.",institutionString:null,institution:{name:"Mexican Social Security Institute",institutionURL:null,country:{name:"Mexico"}}},{id:"74716",title:"Dr.",name:"Ezequiel",surname:"Fuentes-Pananá",slug:"ezequiel-fuentes-panana",fullName:"Ezequiel Fuentes-Pananá",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Nacional de Cardiología",institutionURL:null,country:{name:"Mexico"}}},{id:"74720",title:"Dr.",name:"Vilma-Carolina",surname:"Bekker-Mendez",slug:"vilma-carolina-bekker-mendez",fullName:"Vilma-Carolina Bekker-Mendez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Mexican Social Security Institute",institutionURL:null,country:{name:"Mexico"}}},{id:"84659",title:"Mr.",name:"David Aldebarán",surname:"Duarte-Rodríguez",slug:"david-aldebaran-duarte-rodriguez",fullName:"David Aldebarán Duarte-Rodríguez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"119830",title:"MSc.",name:"Abigail",surname:"Morales-Sanchez",slug:"abigail-morales-sanchez",fullName:"Abigail Morales-Sanchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Mexican Social Security Institute",institutionURL:null,country:{name:"Mexico"}}}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
\n"}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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