\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"2036",leadTitle:null,fullTitle:"Insecticides - Advances in Integrated Pest Management",title:"Insecticides",subtitle:"Advances in Integrated Pest Management",reviewType:"peer-reviewed",abstract:"This book contains 30 Chapters divided into 5 Sections. Section A covers integrated pest management, alternative insect control strategies, ecological impact of insecticides as well as pesticides and drugs of forensic interest. Section B is dedicated to chemical control and health risks, applications for insecticides, metabolism of pesticides by human cytochrome p450, etc. Section C provides biochemical analyses of action of chlorfluazuron, pest control effects on seed yield, chemical ecology, quality control, development of ideal insecticide, insecticide resistance, etc. Section D reviews current analytical methods, electroanalysis of insecticides, insecticide activity and secondary metabolites. Section E provides data contributing to better understanding of biological control through Bacillus sphaericus and B. thuringiensis, entomopathogenic nematodes insecticides, vector-borne disease, etc. The subject matter in this book should attract the reader's concern to support rational decisions regarding the use of pesticides.",isbn:null,printIsbn:"978-953-307-780-2",pdfIsbn:"978-953-51-4382-6",doi:"10.5772/2447",price:159,priceEur:175,priceUsd:205,slug:"insecticides-advances-in-integrated-pest-management",numberOfPages:724,isOpenForSubmission:!1,isInWos:1,hash:"42dc69ce20386f76845e38275b0e54e8",bookSignature:"Farzana Perveen",publishedDate:"January 5th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/2036.jpg",numberOfDownloads:117089,numberOfWosCitations:249,numberOfCrossrefCitations:83,numberOfDimensionsCitations:286,hasAltmetrics:1,numberOfTotalCitations:618,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 22nd 2011",dateEndSecondStepPublish:"March 22nd 2011",dateEndThirdStepPublish:"July 27th 2011",dateEndFourthStepPublish:"August 26th 2011",dateEndFifthStepPublish:"December 24th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,8,9",editedByType:"Edited by",kuFlag:!1,editors:[{id:"75563",title:"Dr.",name:"Farzana Khan",middleName:null,surname:"Perveen",slug:"farzana-khan-perveen",fullName:"Farzana Khan Perveen",profilePictureURL:"https://mts.intechopen.com/storage/users/75563/images/system/75563.png",biography:"Dr Farzana Khan Perveen (FLS; Gold-Medallist) obtained her BSc (Hons) and MSc (Zoology: Entomology) from the University of Karachi, MAS (Monbush-Scholar; Agriculture: Agronomy) and from the Nagoya University, Japan, and PhD (Research and Course-works from the Nagoya University; Toxicology) degree from the University of Karachi. She is Founder/Chairperson of the Department of Zoology (DOZ) and Ex-Controller of Examinations at Shaheed Benazir Bhutto University (SBBU) and Ex-Founder/ Ex-Chairperson of DOZ, Hazara University and Kohat University of Science & Technology. \nShe is the author of 150 high impact research papers, 135 abstracts, 4 authored books and 8 chapters. She is the editor of 5 books and she supervised BS(4), MSc(50), MPhil(40), and Ph.D. (1) students. She has organized and participated in numerous international and national conferences and received multiple awards and fellowships. She is a member of research societies, editorial boards of Journals, and World-Commission on Protected Areas, International Union for Conservation of Nature. 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Topics to be covered include: the importance and challenges of video face recognition in real life scenarios, describing a general architecture of a generic video face recognition system and a working solution suitable for recognizing faces in real-time using low complexity devices. Each component of the system will be described together with the system’s performance on a database of video samples that resembles real life conditions.
\n\t\tFace recognition remains a very active topic in computer vision and receives attention from a large community of researchers in that discipline. Many reasons feed this interest; the main being the wide range of commercial, law enforcement and security applications that require authentication. The progress made in recent years on the methods and algorithms for data processing as well as the availability of new technologies makes it easier to study these algorithms and turn them into commercially viable product. Biometric based security systems are becoming more popular due to their non-invasive nature and their increasing reliability. Surveillance applications based on face recognition are gaining increasing attention after the United States’ 9/11 events and with the ongoing security threats. The Face Recognition Vendor Test (FRVT) (Phillips et al., 2003) includes video face recognition testing starting with the 2002 series of tests.
\n\t\t\tRecently, face recognition technology was deployed in consumer applications such as organizing a collection of images using the faces present in the images (Picassa; Corcoran & Costache, 2005), prioritizing family members for best capturing conditions when taking pictures, or directly annotating the images as they are captured (Costache et al., 2006).
\n\t\t\tVideo face recognition, compared with more traditional still face recognition, has the main advantage of using multiple instances of the same individual in sequential frames for recognition to occur. In still recognition case, the system has only one input image to make the decision if the person is or is not in the database. If the image is not suitable for recognition (due to face orientation, expression, quality or facial occlusions) the recognition result will most likely be incorrect. In the video image there are multiple frames which can be analyzed in order to have greater recognition accuracy. Even if some frames are not suitable for recognition there is a high probability that some of them will work and the decision made will have a high degree of confidence. Once a face is recognized, it remains recognized in the scene by tracking techniques.
\n\t\t\tThe disadvantage in the video imaging technique is in most cases the quality and size of the input frames are inferior compared to the still images.
\n\t\t\tMost face recognition systems for still and video image technology follow the same classical workflow:
\n\t\t\t\tThe faces have to be detected in the images.
The faces are normalized to the same size and usually same in-plane orientation.
Before or after (2), a pre-processing step tries to minimize the effect of illumination over the face.
Features are extracted from the facial region.
Test faces are compared with a database of people.
The first difference between the video and still image technology is that video scenarios can use a tracking algorithm together with a detection algorithm in order to keep track of all the faces in the video sequence. Using face tracking combined with face detection has three main advantages:
\n\t\t\t\tIt allows the system to follow the faces across a wide range of variations in pose and lighting where tracking can be done easier than detection.
The time and memory requirements of a face tracking algorithm are lower than those of a face detection algorithm. Freed resources can be accessed once a face is detected in a frame. Tracking from that moment forward is a very important aspect when achieving real-time functionality.
Once a face in a particular frame is recognized with a high degree of confidence, that particular face does not need to be processed for the next frames. Only track the face and keep the association between the recognized person and the tracked face.
In the classification stage of the video imagery, a history of the recognized face offers greater accuracy than that of a still image.
\n\t\t\t\t\n\t\t\t\t\tFigure 1 shows a typical architecture of a video face recognition system.
\n\t\t\t\tBelow are brief descriptions of each component together with the requirements that need to be satisfied in order to have a robust real-time face recognition system which can be integrated into an embedded device.
\n\t\t\t\tThe face detection and tracking component is very important in designing the recognition system. The properties of the detection algorithm (detection rate, robustness to variations, speed and memory requirements, etc.) will directly affect the properties of the overall recognition system. It is clear that undetected faces will not be recognized. Also considering the goal of real-time functionality on embedded devices where limited resources are available, spending most of that early on will reduce the application of the other blocks in the diagram in real time.
\n\t\t\t\t\tThe main challenges associated with the detection and tracking algorithm are determined by the following factors:
\n\t\t\t\t\tArchitecture of a VFR system
Face orientation (pose). The appearance of the face may differ in many ways when the orientation of the face changes from frontal to profile or extreme view angles, where face components like the eyes, the nose or the ear may be occluded. It is difficult to detect a face at these extreme angles although face tracking is achievable.
Changes in facial appearance. Examples include beards, moustaches or glasses. Women may use make-up which can significantly alter the face color and texture. These factors together with the potential for variability in shape, size or color of the face makes face detection challenging.
Facial expression. The appearance of the face is directly affected by the person\'s facial expression. Tracking has to be robust to these variations as it is likely to be encountered in normal consumer videos.
Occlusions. Different components from the face may be occluded in the image by other objects or faces. These have to be addressed by the tracking algorithm.
Capture conditions. Factors that are involved in capturing the image such as lighting conditions, camera characteristics or quality of the captured image may have a big influence in the detection process.
Face size or distance to subject. For video face detection and tracking consider the capture resolution and the distance from the capture equipment to the subject. For normal working resolution (qVGA, VGA) the faces can be very small even for relatively short distances.
The detection algorithm should have a high detection rate and robustness to variations such as changes in appearance, capture conditions and face size. The tracking algorithm should improve the robustness to face orientation, expressions and occlusions.
\n\t\t\t\t\tAll the above requirements are difficult to fulfil especially for real-time scenarios in embedded devices. In the last few years there has been much progress in this area and now face detection and tracking is a common feature in most consumer cameras and mobile phones.
\n\t\t\t\t\tTessera’s OptiML™ Face Tools Face Tracking and Face Recognition technologies represent a perfect example of state-of-the-art technology in this area.
\n\t\t\t\t\tSome of the relevant parameters of Tessera’s Face Tools technology that affect the performance of the overall recognition system include:
\n\t\t\t\t\tFace tracking for up to 10 faces per frame, with less than 0.1 seconds lock time
Minimum face size: 14x14 pixels
Real-time face tracking up to 30 frames per second
Faces detected in a wide range of orientations including rotation-in-plane and out-of-plane
Together with the detection rate, another metric used to describe the performance of a face detection algorithm is the false positive rate which represents the number of regions falsely reported as faces by the algorithm.
\n\t\t\t\t\tThe false positive rate is not as important as the detection rate because the recognition algorithm should be able to differentiate between faces and non-faces when trying to classify the false positive candidates.
\n\t\t\t\tIt is very important to detect and track the faces in all conditions and variations. When comparing local regions between faces, an image registration step must be performed so corresponding facial features are synchronised.\n\t\t\t\t\t
\n\t\t\t\t\tSimple geometric normalization usually involves bringing the faces to a standard size and rotating them in-plane in order to bring the eyes on the same horizontal line. Figure 2 shows some face samples before and after applying the geometric normalization.
\n\t\t\t\t\tMore complex normalization scenarios (Corcoran et al., 2006b) can use 3D face models to rotate the face in the out-of-plane space to have identical orientation (i.e only frontal faces). This will have a higher computational requirement and could only be used when there is enough processing power. Figure 3 shows an example of the output of this complex normalization which can help recognition for large pose variations.
\n\t\t\t\t\tAll other processing steps applied after geometric normalization should have the same affect on each face.
\n\t\t\t\t\tGeometric normalization (Before (top), After (bottom))
Complex geometric normalization
If we can control the image capturing environment and impose strict requirements regarding lighting conditions (i.e. control access), recognition accuracy can be improved. In most scenarios where video face recognition is employed, the variations in lighting conditions when the faces are captured can range between dark and bright extremes. The profile face samples for each person to be recognized are captured in very different conditions with still images than those used in video imagery. A pre-processing algorithm should be used to minimize the effect of the lighting conditions when capturing the video images.
\n\t\t\t\t\tDepending on the resources available and capturing conditions, the illumination normalization algorithm can vary from simple algorithms such as: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) (Pizer et al., 1987; Corcoran et al., 2006a), logarithm transformed combined with suppressing DCT coefficients (LogDCT) or retinex (Land, 1986) based approaches, to more complex algorithms that can model the effects of lighting over facial regions (Lee et al., 2001; Smith & Hancock, 2005).
\n\t\t\t\t\tFor embedded devices the simple normalization is a good compromise between execution speed and robustness to lighting variations.
\n\t\t\t\t\t\n\t\t\t\t\t\tFigure 4 displays the output of simple normalization techniques for two images affected by extreme side illumination.
\n\t\t\t\t\tAn important issue to be considered when designing an illumination normalization algorithm is the balance between minimizing the effect of illumination and the inherent loss of information; information which is useful for classification. For instance, a face may appear dark because of dark lighting conditions or because the person has dark skin. Usually after normalization this information may not be recovered.
\n\t\t\t\t\tSimple illumination normalization examples
The validation sets for most of the algorithms that try to minimize the effect of lighting (i.e. Yale database (Georghiades et al.,2001) ) consists of faces captured in very different lighting conditions where the normalization algorithms have better results compared with using the original faces without illumination normalization. In real life conditions, the faces can be compared to similar lighting conditions where applying the illumination normalization should not have a negative impact over the recognition results.
\n\t\t\t\t\tIt is very important to have a validation set that has a variation distribution close to those most likely to be encountered in the scenarios that the recognition system is designed for.
\n\t\t\t\tTogether with the useful information that can be used to differentiate between individuals, the face images described by the pixel values contain redundant information and information that can be ignored in the classification stage. By extracting only the useful information in this step we improve the accuracy of the recognition and also lower the storage requirement for each face.
\n\t\t\t\t\tBelow are the main requirements for the feature selection algorithm:
\n\t\t\t\t\tGood discriminative property. The features need to be able to differentiate between people. This translates into large variations between the value distributions for each person.
Consistency. Features should not be modified between different images of the same person. This allows for recognition accuracy across large variations. Quantitatively this translates into small variation in the feature distribution for multiple faces of the same person.
Small size. These features need to be stored and compared. Small size will allow fast comparison and low storage requirement.
Fast computation. In order to achieve real-time recognition in video images, the faces need to be processed quickly.
The first two requirements will improve the accuracy of the recognition system and the last two requirements will ensure real-time recognition in embedded devices.
\n\t\t\t\t\tClassical approaches for still image recognition were also applied to the video image scenario with good results. These include: Principal Component Analysis (PCA) (Turk & Pentland, 1991), Linear Discriminate Analysis (LDA) (Belhumeur et al., 1996) and Discrete Cosine Transform (DCT) (Podilchuk & Zhang, 1996).
\n\t\t\t\t\tThe DCT approach is of particular interest because of the speed of DCT transformation. Most of the capturing devices have DCT already implemented as part of JPEG compression module for storing captured images. More recent approaches like Local Binary Patterns (LBP) (Ojala et al., 2001) and Histogram of Oriented Gradients (HOG) (Lowe, 2004) have been used for face recognition.
\n\t\t\t\tIn the case of still image recognition, the system makes a decision if the test face belongs to one of the people in the database and if so, which one (based on comparing the features computed in the previous step for test faces and a database of people).
\n\t\t\t\t\tIn the case of video image recognition, the system compares the series of test faces with those in the sample database. Most commonly this is implemented as a series of still images derived comparisons and at each frame the confidence of our decision is modified based on the history of previous comparisons.
\n\t\t\t\t\tSimple classification algorithms like distance between feature vectors are preferred because of their simplicity and speed. More complex learning algorithms can be used if there are enough computation resources.
\n\t\t\t\t\tThe classification algorithm is divided into two stages:
\n\t\t\t\t\tTraining. Prototypes are constructed for each person in the database. The prototypes can be built from single or multiple face samples. Using multiple samples improves the quality of the prototype. The prototype can be represented by a series of feature vectors (such as distance-based classification) or can be represented by statistical models trained with multiple samples (such as learning-based classification algorithms).
Testing. Test samples are compared with each person prototype and similarity scores are computed. A decision is made using these similarity scores and the history of previous scores.
If, for a specific scenario, there is a fixed database that does not modify or update on the same platform where recognition is executed, a more complex algorithm for training can be used (i.e. training a learning algorithm) and performed offline. The result of this training algorithm is used during the recognition phase. When the database needs to be updated on-line at any time, the training algorithm needs to be less complex to be run on the embedded device. The result of the training algorithm, either the feature vectors or the person model, needs to be stored in the training database. This will influence the storage requirement of the recognition system.
\n\t\t\t\t\tAn interesting algorithm that can be used for video face recognition is to model not only the appearance of the person at each frame but also the transition from frame to frame. A multi-dimensional Hidden Markov Model (HMM) (Nefian & Hayes, 1998) is used in order to model this type of transition. At the moment the complexity of HMMs makes it less favourable for embedded implementation.
\n\t\t\t\tComparing two face recognition systems is a difficult task because there are many parameters that can describe the performance of a particular recognition system. Usually one system or the other is superior using different sets of parameters.
\n\t\t\t\tDepending on the specific application where the recognition system is deployed, some specific performance metrics are more important than others. For example, a security system based on face recognition will have, as a main priority, a very low false acceptance rate, whereas a photo sorting application implemented on an embedded platform based on face recognition, will have its priorities of high recognition rates and low complexity.
\n\t\t\t\tThe performance of a recognition system can be described by two types of parameters: accuracy parameters that describe how accurate the system is in recognizing faces and technical parameters that represent characteristics such as how fast the system will process a face, etc.
\n\t\t\t\tSome of the accuracy parameters that can be used to describe a video face recognition system include:
\n\t\t\t\tRecognition rate. This is the main measurement to describe the accuracy of a recognition system. It represents how many faces are correctly recognized from the total number of faces. For video recognition this is a little more complex as it can be computed as the total number of frames where the faces are recognized.
False positive rate. For specific applications this parameter can be more important than the recognition rate. This is usually computed as the number of mistakes made by the system. It can be further classified as a false acceptance rate in verification applications where an unknown individual is classified as one person from the database and as a false rejection rate where a person from the database is classified as unknown.
Receiver Operating Characteristic (ROC) curve. In most cases there is a trade-off between the recognition rate and a false positive rate. For a high recognition rate, tune the recognition system to increase the recognition rate. This will inevitably increase the false positive rate as well and the other way around. The ROC curve represents the recognition rate for each possible false positive rate and only by displaying the ROC curves can a comparison of the two recognition algorithms be made.
Minimum face size to be detected and recognized. When working with normal video resolution the faces can be very small, even at short distances from the capture equipment. Imposing a high minimum size for the face in order to be recognized can lead to a high rate of faces that are ignored or not recognized in the video images.
Range of pose variations to recognize a face. Depending on the application to apply the recognition, a higher or lower range of pose variations is needed to recognize the faces.
There are many technical parameters that can be used to describe a video face recognition system including:
\n\t\t\t\tProcessing time. This represents the time required to detect, process and classify all faces in a frame. This parameter depends on the platform where the recognition is implemented and will dictate if real-time functionality is available or not. For video frames, the time available for real-time recognition is the time between consecutive frames.
Memory requirements. This represents the storage requirement for the system and includes the size of the feature vectors, person prototypes and other constants used in the algorithm.
Number of faces recognized in each frame. The time required for detecting all faces in a frame is constant. This parameter will be influenced by the time required to process and recognize one face after it is detected.
The accuracy parameters depend on the database used for testing and the technical parameters depend on the specific platform where the recognition system is implemented. Without using the same database and same platform, two recognition systems cannot be compared only by the performance parameters.
\n\t\t\tThe goal is to build a video face recognition system running in real-time on low computational power embedded platforms capable of recognizing multiple faces in video sequences. The main use case scenario intended for this system is tagging faces in consumer images as they are captured by digital cameras and mobile phones. The main requirements for the recognition system are high recognition rate, high robustness to variation types and low computational complexity for the algorithms used in the system. For this scenario, the input video stream can vary in size from small (qVGA) to high (full HD). Faces can also vary in size from tens of pixels in width to hundreds of pixels. Large variations in face pose, expression and illumination are also likely to be present.
\n\t\t\tTessera’s Face Tracking and Detecting technology is used in this experiment (Tessera, 2010). For geometric normalization, a computationally attractive approach is used, which involves: gray-scale transformation of the image, rotation of the face image to align the eyes on horizontal direction and resizing the face image to a small fixed size (i.e. 32x32 pixels). This size will allow for recognition of faces at a range of distances from the camera.
\n\t\t\tTo minimize the effect of lighting variations, use a variant of the retinex (Land, 1986) illumination normalization algorithm. This is done by using a fixed variance matrix computed offline from a large database of images. This approach is very fast to apply and insures that the features computed in the next stage are more robust to large variations in illumination.
\n\t\t\tThe features used for classification, in this chapter, are a variant of the Local Binary Pattern (LBPs) (Ojala et al., 2001) features which have been recently employed, with good results, for face recognition (Ahonen et al., 2006).
\n\t\t\tThe classic approach of using LBP features in face recognition involves computing these features for each pixel in the face image, dividing the face image into small regions (separated or overlapped), and for each region computing the distribution of the LBP values. Often, only a small subset of all the features is used (uniform LBPs) in order to compute the region distribution. The classification involves comparing these distributions between corresponding regions from the test faces and the face samples used to build the prototypes in the training stage.
\n\t\t\tOne approach is completely different. It is based on selecting from all possible features, those features that maximize the two properties defined in Section 2.1.4, namely: consistency and discriminancy. The training stage is split into two stages:
\n\t\t\tOff-line training, using a very large database of faces, in order to determine and select the most consistent features.
On-line training, using the face samples in the database that need to be recognized.
The weights for each selected feature are computed in the off-line stage, each weight representing how discriminating the respective features are for the people in the database. Look for the best features that are globally consistent for a very large database (off-line) of people and weight them according to how well they can discriminate for a given database (on-line). Both training stages are presented in the next sections.
\n\t\t\tFor classification a similarity measure between two faces is computed by looking for identical corresponding features in the off-line training stage. For each identical feature value add the similarity between the energy of the features multiplied by the weights computed in the on-line training stage.
\n\t\t\tThe Local Binary Patterns (LBP) (Ojala et al., 2001) features have been used in the system. These features are computed based on comparing the central pixel with its neighbours, concatenating the binary comparison results and computing the decimal number from the binary string.
\n\t\t\t\t\n\t\t\t\t\tFigure 5 shows how the feature is computed from an image.
\n\t\t\t\tLBP feature.
The LBP features extract local information from the face region and due to the binary comparisons are robust to changes in illumination.
\n\t\t\t\tIn order to capture more information from the face region, features are computed at different face resolutions beginning with the standard size used for geometrical normalization, down to smaller scales by downsampling the face image with different factors (i.e. 2, 4 etc). The LBP feature are extracted at each resolution using their 8 closest neighbours together with their extended variants using 8 more distant neighbours. Figure 6 illustrates an example of the normal LBP feature together with its first order extended LBP feature.
\n\t\t\t\t\tLBP and Extended LBP
For each pixel in the normalized face image, compute multiple feature values. Do the same for the other scales.
\n\t\t\t\tThe binary comparisons used for computing the features make them very robust to illumination variations. They also cause loss of information about the similarity of the local regions. For example, a very strong feature will have same value as a very faded feature. For this, calculate the normalized energy of the feature that will be used when comparing identical features for similarity between faces. The energy is computed using the formula:
\n\t\t\t\t\twhere I\n\t\t\t\t\t\t\n\t\t\t\t\t\t\ti\n\t\t\t\t\t\t represents the value of the neighbour pixel i used when computing the feature.
\n\t\t\t\t\tThe energy is computed for both normal LBP and extended LBP.
\n\t\t\t\t\tBecause small size face images are used, the features are not grouped in the face image by dividing the face into regions, but the corresponding features are compared for classification. The features are corresponding if they are computed at the same location, same scale and if they are normal or extended.
\n\t\t\t\t\tThe feature vector after this analysis consists of the normal and extended LBPs computed at each location and at each scale together with their energies.
\n\t\t\t\tGood features are those that do not change between images of the same person. This increases the accuracy of the recognition for different variations of the facial image.
\n\t\t\t\tThis algorithm ranks a set of features given a large database of facial images. The order of the features is given considering their intra-class variation from low to high. The first features will be the most consistent between faces of same individuals. For recognition these features are more robust to variations.
\n\t\t\t\tAssume there is a collection of m people (P\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t\n\t\t\t\t\t, P\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t\n\t\t\t\t\t,...P\n\t\t\t\t\t\n\t\t\t\t\t\tm\n\t\t\t\t\t), each with multiple facial images. For each face compute all N possible features described in the previous section (normal and extended LBPs at all resolutions). Note the feature vector for person P\n\t\t\t\t\t\n\t\t\t\t\t\ti\n\t\t\t\t\t image j as:
\n\t\t\t\tThe features in the system are the LBP features. This algorithm can be extended to any other type of feature or combinations between features. Below is the general form of the algorithm.
\n\t\t\t\tFor each feature, define a measure of intra-class consistency S=(S\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t\n\t\t\t\t\t,S\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t\n\t\t\t\t\t,…,S\n\t\t\t\t\t\n\t\t\t\t\t\tN\n\t\t\t\t\t\n\t\t\t\t\t)\n\t\t\t\t
\n\t\t\t\tThe steps of the algorithm are:
\n\t\t\t\tReset all scores.
Update scores. For every feature k, for every person i, for every m image of person i:\n\t\t\t\t\t\t
Compare the feature F\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\timk\n\t\t\t\t\t\t\t with the same feature k of the remaining j images of person i (F\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tijk\n\t\t\t\t\t\t\t with j=1:N\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t) where N\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t is the number of images for person i.
If |F\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\timk\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t- F\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tijk\n\t\t\t\t\t\t\t| < thr then increment S\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tk\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t
At the end of this process order all features according to their score. Depending on the constraints, either keep a fixed number of features for classification in the latter stage or impose a threshold over the consistency measure.
\n\t\t\t\tThe |F\n\t\t\t\t\t\n\t\t\t\t\t\timk\n\t\t\t\t\t\n\t\t\t\t\t- F\n\t\t\t\t\t\n\t\t\t\t\t\tijk\n\t\t\t\t\t| term represents the distance between the feature values. In this case, search for identical features so thr = 0. For other types of features, a distance measure needs to be defined and a suitable thr needs to be chosen.
\n\t\t\t\tFor best results, meaning best globally consistent features, the input database should be very large with all types of variation. Because it is executed off-line it does not affect the speed performance of the overall system.
\n\t\t\tTogether with consistency, the features also need to be able to discriminate between the people in the database. The same value for one feature across a database means both perfect consistency and no discriminative power. This algorithm assigns weights to each previously selected feature. The weights determine how well the feature can discriminate between the person in the database and which can be used in the classification stage.
\n\t\t\t\tApply this algorithm to any type of feature using a suitable distance measure. The algorithm description is generic.
\n\t\t\t\tAssume m people in the database (P\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t\n\t\t\t\t\t, P\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t\n\t\t\t\t\t,...P\n\t\t\t\t\t\n\t\t\t\t\t\tm\n\t\t\t\t\t), each with at least one representative facial image. For each face, N representative features selected by the off-line training procedure and their corresponding discriminative scores. For example, for person P\n\t\t\t\t\t\n\t\t\t\t\t\ti\n\t\t\t\t\t we have:
\n\t\t\t\tfeature vector Fi=(Fi1,Fi2,…,FiN)
score vector Si=(Si1,Si2,…,SiN)
The steps of the algorithm are:
\n\t\t\t\tReset all scores.
Update scores. For every feature k, for every person i,
Compare the feature F\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tik\n\t\t\t\t\t\t\t with the same feature k of the remaining people (F\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tjk\n\t\t\t\t\t\t\t with j=1:N)
If |Fik - Fjk| > thr then increment Ski\n\t\t\t\t\t\t
In order to make these scores independent of the number of people and faces in the database, normalize them using the maximum sum of scores for a person using the next equation where N represents the number of features and m the number of people in the database:
\n\t\t\t\tThe same observations from the previous section for the terms: |F\n\t\t\t\t\t\n\t\t\t\t\t\tik\n\t\t\t\t\t\n\t\t\t\t\t- F\n\t\t\t\t\t\n\t\t\t\t\t\tjk\n\t\t\t\t\t\n\t\t\t\t\t| and thr are valid. In the case of searching for identical LBPs, the parameter thr = 0.\n\t\t\t\t
\n\t\t\tHaving computed the features described in Section 3.1 and the discriminative scores of all features selected in Section 3.2, using the algorithm described in the previous section, compute a similarity (S\n\t\t\t\t\t\n\t\t\t\t\t\tij\n\t\t\t\t\t) between two faces (trained face f\n\t\t\t\t\t\n\t\t\t\t\t\ti\n\t\t\t\t\t and test face f\n\t\t\t\t\t\n\t\t\t\t\t\tj\n\t\t\t\t\t) by counting how many identical corresponding features there are between the two faces using this formula:
\n\t\t\t\twhere:
\n\t\t\t\t\n\t\t\t\t\t\t\tg\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tijk\n\t\t\t\t\t\t\t is equal to 1 if feature k is identical between faces i and j, and 0 otherwise.
\n\t\t\t\t\t\t\te\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tik\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t, e\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tjk\n\t\t\t\t\t\t\t represent the energy of the feature k from image i and j respectively computed using eq. (1).
\n\t\t\t\t\t\t\tw\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tk\n\t\t\t\t\t\t\t represents the discriminative score for features k of trained face i.\n\t\t\t\t\t\t
Comparing the test face with all face samples from the trained database, return the most similar person with the test face. By imposing a decision threshold, control the recognition rate versus the false positive rate, depending on the application mentioned in Section 2.2. Once the similarity measure between the test face and the most similar person from the database is higher than the decision threshold, decide that the face is recognized and continue or not the recognition process over the next frames.
\n\t\t\tIn order to assess the performance of the recognition system a large database of videos with systematic variations was used, including: pose, illumination, face size/distance to subject, and facial expressions.
\n\t\t\tIn the training stage, a single image was used to train each person. The training face is frontal, good size, normal illumination and good quality. Tests were run for different numbers of people in the database from low (3) to high (100).
\n\t\t\tFor each test, the recognition rate (RR) was measured, as the number of correct classifications, false positive rate (FP2) as wrong classifications and undecided rate (MD) as number of test faces which were not classified.
\n\t\t\t\n\t\t\t\tFigure 7 shows the recognition and error rates as a function of head yaw angle. As specified above, training was conducted at head yaw angles of zero degrees and testing was done with 0˚, 10˚, 20˚ and 30˚ yaw angles.
\n\t\t\tRecognition performance for different yaw angles
\n\t\t\t\tFigure 8 shows the recognition and error rates as a function of head pitch angle. Training was conducted at head pitch angles of zero degrees.
\n\t\t\t\n\t\t\t\tFigure 9 shows the recognition and error rates as a function of different facial expressions. The training faces had no facial expression.
\n\t\t\t\n\t\t\t\tFigure 10 shows the recognition and error rates as a function of different illumination conditions. The approximate EV values for the given conditions are: LowLight (2.4EV) and StrongLight (9EV), which can be considered extreme lighting conditions. Training was conducted using normal indoors ambient lighting.
\n\t\t\tRecognition performance for different pitch angles
Recognition performance for different face expressions
The main technical parameters for the system were implemented on an ARM9 platform (266 MHz CPU), the processing time for a face depending on the size of the input frame varied between 8 and 15 milliseconds for qVGA and VGA input frame size which is well within real-time requirements. The size of the features vector for each analyzed face is about 2Kb which is very small.
\n\t\t\tRecognition performance for illumination conditions
This chapter presented the challenges of implementing a real-time video face recognition system on an embedded platform. The first section presented the main issues that need to be addressed when designing such a system and possible solutions. The second part described a working solution based on using LBP features which are fast to compute, robust to variations and able to extract useful information from the face region. In order to obtain a robust recognition system, only the features which have the same value across multiple variations of the same person were extracted. In order to increase the accuracy of the system, weights were associated to the selected features based on their discriminative power between the people from the database.
\n\t\t\tResults for this system were tested and implemented on an embedded platform, which shows good accuracy across large variations of the input data and technical parameters which satisfy the condition for real-time processing.
\n\t\tWith the unprecedented amount of data being generated today, we must start (and in some cases have already started) to rely on automatic systems to process, analyse, and understand all the scientific information that we produce. For some examples in chemistry, consider the number of drugs represented in DrugBank, which grew from 3909 in 2006 to 9688 [1], about 13% each year; the number of metabolites in the Human Metabolite Database grew from 2180 in 2007 to 114,100 in 2017 [2], approximately 39% per year (although at some point this database imported a large number of metabolites at once, artificially increasing this statistic); ChemSpider had 25 million compounds in 2010 [3] and now has 63 million (10% a year); and PubChem grew from 19 million compound structures in 2008 [4] to 96.5 million in August 2018 [5] (16% a year). These numbers usually grow exponentially [6], reflecting the fact that the amount of knowledge the scientific community produces is proportional to the amount of knowledge we discover.
With such high volumes of data, it is imperative that we categorise this information in ways that assist us in the tasks of consuming that information, specifically through categorisation schemas that abstract away the less useful details of reality and increase the manageability of this information. As we will see later in this chapter, ontologies can perform that goal: they are computational artefacts (files, tables in a database, etc.) whose goal is to encode real-world knowledge in machine-readable logical axioms that can be used by automatic systems to manipulate the knowledge inferred and potentially derivable from the data we have.
Furthermore, like most other scientific knowledge, chemistry ideas and notions are inferred from comparing entities and finding their similarities and differences. For instance, compound similarity has been used to (i) develop pharmacophores [7, 8], (ii) estimate whether a compound is harmful without in vivo experimentation [9], (iii) understand the evolution of metabolic pathways [10], (iv) predict adverse side effects of drugs [11], and (v) perform pharmacological profiling of compounds in drug design [12].
As we explore in this chapter, ontologies provide one way to measure similarity of chemistry entities (compounds, substances, mixtures, reactions, etc.), a technique known as ontology-based semantic similarity (shortened to semantic similarity in this chapter). This idea is already widely used in genomics and proteomics, but its full potential still needs to be brought over to other domains. While some research has successfully used this methodology in the cheminformatics domain (which we discuss below), there is still space for improvement and further methodological development.
In this chapter, we explore the ideas and concepts behind semantic similarity and chemistry ontologies, explore some past applications that use those concepts to further our knowledge of the chemical domain, and expose some limitations and challenges that this technique still needs to overcome for its whole potential to be released.
Similarity, in its nature, is a notion that produces a number. In that sense, it is mathematical. However, chemical knowledge cannot be trivially reduced to mathematical form. For example, given two molecules, how should one compare them and assign a number to represent their similarity? And even if specific cases can be handled by humans, we still need an automatic way to perform comparison. However, to a certain extent, computers can only manipulate objects that can be represented mathematically (e.g., vectors) or as strings of characters (e.g., gene sequences, SMILES). But the algorithms that are used with these structures are context-free: they usually transform the structures without any knowledge of what they represent.
Many mechanisms exist to deal with this issue. For example, graph similarity can be used to find common substructures in two molecules as a basis for similarity calculations (see, e.g., [13, 14]), but these methods tend to be slow and computationally expensive. There is also the possibility to reduce a molecular structure into a fingerprint, which is a binary vector where each position represents the presence (with a 1) or absence (with a 0) of a certain feature in the structure. For example, the presence of a carboxyl group could be indicated with a 1 in some position of the vector. Similarity can then be computed by measuring the overlap in those vectors [15, 16].
These methods provide a high similarity value when the structures of the two molecules are high. Under the quantitative structure-activity relationship (QSAR) premise, this means that, in general, two molecules with a high similarity score (as defined by these methods) tend to have similar biological role, similar chemical properties (such as melting point, optical parameters, and mass spectroscopy spectra), similar safety warnings, similar appearance, etc. But this is not always true. For instance, while L-amino acids are used to synthesise proteins, D-amino acids are much less frequent in nature, and their role is quite different [17]. From a biological point of view, they are distinct; however, to capture their structural differences, one needs to use three-dimensional methods, and even with that consideration, the structural similarity will be high, because both molecules have the same atoms and bonds. Another possibility includes simulation of docking with target proteins, but these methods are quite expensive computationally. Furthermore, not only can similar molecules perform different biological roles, different molecules can perform similar roles. For example, both clavulanic acid and salsalate are β-lactamase inhibitors, despite their different structures (see Figure 1).
Chemical structure of two semantically related compounds. The two molecular structures in the figure are quite different structures, and yet both present the same biological activity, namely, they inhibit β-lactamase enzymes.
Another way to measure similarity is by means of the semantics attached to the chemical compounds. Here, we use the term semantics to mean the knowledge that exists about a compound. This includes not only the structure of the molecule itself (e.g., the atomic connectivity, the number of oxygen atoms, the presence of triple bonds) but also other types of contextual knowledge, such as its chemical role (e.g., whether it is an electron donor, a solvent, or an explosive), biological role (e.g., whether it is a poison, a cofactor, or a vitamin), its applications (as a drug, fertiliser, fuel, etc.), its relationship to other molecules (such as being enantiomers, parent hydrides, etc.), and so on.
The difficulty with this is that knowledge is not directly machine-readable. Indeed, established facts have been traditionally published in plain text, which enables some humans to understand them; however, natural language processing techniques are not yet fully capable of converting scientific text into actionable formats (e.g., formats that allow automatic reasoning). Therefore, to enable the application of computerised processing power to knowledge manipulation, it is essential to find ways to represent knowledge in machine-readable formats.
Ontologies are the solution to this problem. An ontology is a representation of concepts from a domain of knowledge and the relationship between them and is usually visualised as a directed acyclic graph (DAG), where nodes are the concepts, edges are the relationships, and there are no cycles in the graph. See, for example, Figure 2, a toy exampled based on a real-world ontology that encodes the fact that “acetate” is the conjugate base of “acetic acid” and that “acetic acid” is the conjugate acid of “acetate” and then organises these concepts in a hierarchy that contains concepts like “ion”, “molecule”, “organic acid”, and “organic molecular entity”, and ends up in the most generic “molecular entity” concept.
A toy example of an ontology for chemical compounds, based on ChEBI. The ontology shows “is-a” relationships with solid lines, and a relationship between acid/base conjugates with a dotted line. The green shaded concepts are those that subsume both the yellow and the blue ones.
There are many ontologies whose purpose is to encode the chemical knowledge, but one of the most comprehensive and used is the ontology for Chemical Entities of Biological Interest (ChEBI) [18]. This ontology represents in a machine-readable format about 114 thousand concepts, including not only the chemical compounds but also their biological and chemical roles. Other ontologies that encode this or related domains include (i) Interlinking Ontology for Biological Concepts, (ii) Current Procedural Terminology, (iii) SNOMED CT, (iv) Chemical Information Ontology, and (v) Chemical Methods Ontology.
It is important to notice that, even though the notion of ontologies usually requires some logic concepts (such as axioms, predicates, etc.), some classification hierarchies are also sometimes named “ontologies”. MeSH, the system used by PubMed to classify publications, is a hierarchy of concepts that possesses many of the same properties that ontologies do, namely, that it can be represented as a directed acyclic graph. However, one of the differences is that the relationship between two concepts does not always carry the same meaning. For example, “Head” is categorised under “Body Regions”, and “Ear” is categorised under “Head”, but while heads are body regions, ears are not heads; they are instead parts of the head. This illustrates the informality of MeSH: only one relationship type exists and it is used to express different notions. Another system in this category is the Anatomical Therapeutic Chemical (ATC) Classification System.
BioPortal [19], a repository of ontologies for the biomedical domain, contains a collection of 948 ontologies at the time of this writing. As an illustration of its magnitude, consider that 19 ontologies represent the concept “lidocaine”. This reflects the effort being currently spent to represent human knowledge in machine-readable ontologies. In fact, while ontologies such as ChEBI are massive, BioPortal allows their users to submit new ontologies, even if small, focussed on a specific domain, and created with a specific application in mind other than pure knowledge representation (e.g., there is an ontology specific for cardiovascular drug adverse events, with 3 thousand concepts).
Other efforts have been set into place to aggregate ontologies in a single source of knowledge. For example, the Open Biological and Biomedical Ontology (OBO) Foundry [20] developed the OBO file format to represent ontologies and currently defines principles of quality for ontologies in biomedical domain that prescribe good practices for ontology development, such as being open, being reusable, being developed with collaboration in mind, containing both textual and logical definitions (for the benefit of both humans and machines), etc. They contain more than 200 ontologies as of this writing, 10 of which fully adhere to those principles (ChEBI being one of them). The OBO Foundry is tightly coupled with Ontobee [21], a web service that uses the principles of linked data to serve as a linked data server specifically targeted for ontologies and their concepts.
Using a formal representation of knowledge, computers are given the ability to manipulate concepts that are difficult to represent, in a way that preserves their “semantics”. Ontologies provide the appropriate support for automatic manipulation of information. In this context, semantic similarity is a technique that assigns a numeric value to a pair of concepts based on the similarity of their meaning, extracted from the ontology.
For example, there is no directly obvious way to compare two roles. However, considering the illustration in Figure 3, it is possible to intuitively understand that, because both “hallucinogen” and “antifungal drug” are examples of “drugs”, they are more similar than “hallucinogen” and “fossil fuel”. This measure makes use of the meaning of the concepts, implicitly represented in the ontologies through the relations between the concepts. Ontologies function as a proxy for that meaning and enable its manipulation and ultimately comparison.
A second toy example of an ontology representing chemical roles, also based on ChEBI.
Several formulas and ideas have been proposed, implemented and tested in the past to compute semantic similarity. A full exposition on such measures and algorithms is beyond the scope of this chapter. The reader is encouraged to expand on this topic by reading works such as [22, 23, 24, 25]. As such, the following is an abridged version of how ontology-based semantic similarity has been computed. In this discussion, consider the ontology in Figure 3.
Measures of similarity based on ontologies can roughly be divided into edge-based and node-based. An example of an edge-based measure is counting how many relations must be traversed to connect the two concepts being compared. Rada et al. [26] define distance as the number of edges in the smallest path between two nodes composed only of “is-a” relations. In this case, the distance between “hallucinogen” and “antimicrobial agent” would be three (“hallucinogen”→“drug”→“antifungal drug”→“antimicrobial agent”). While this type of approach is intuitive, it assumes that all nodes and all edges are equally important in terms of their semantics (e.g., that all edges weigh the same), which is generally not true in ontologies in life sciences. For instance, the “is-a” relation between “hallucinogen” and “drug” does not necessarily convey the same amount of information as the inverse “is-a” relation between “drug” and “antifungal drug”.
One way to solve this is to introduce node-based methods, a technique that weighs nodes based on their information content (IC) [27]. The IC of a node is a numeric value based that reflects how informative its presence is and is calculated based on its frequency of use, since concepts that appear more frequently are generally less informative. The first formula proposed to measure IC was
where f(c) is the relative frequency with which the concept c and all its descendants appear in a corpus (in the example ontology, we can use the fraction of chemical concepts in ChEBI annotated as performing each of those roles). The intuition behind this idea is the following: consider a document (e.g., a scientific article) that uses the sentence “rodents have fur”. The term “rodent” is used in such a way that every other concept that can be categorised under it also possesses the declared property. In fact, whenever a concept is used (in text, in logical axioms, etc.), it must be interpreted as including the set of all concepts recursively categorised under it.
The similarity between two concepts can be computed as the IC of the most informative common ancestor (usually abbreviated as MICA) between them
This idea has been iterated upon with some additions and adaptations.
The IC measure can be normalised so that it ranges from 0.0 to 1.0 (originally, the measure is unbounded above);
The IC measure has been computed from multiple sources, such as (i) text corpora (as in the original), (ii) frequency of usage of the ontology concepts in external sources [28], or (iii) the ontology itself, where frequency can be computed based on the number of descendants (direct or indirect) of a concept [29], the number of leaf descendants of a concept [30], or other topological properties of the graph representation of the ontology [31].
The semantic similarity measure itself can be normalised. Notice that the original measure gives the same similarity to the pair “application”/“biological role” (both generic concepts) and “fossil fuel”/“antiviral agent”, which goes against the intuition that the first pair should be more similar. Lin [32] uses this idea to define
The notion of shared information content (originally measured as the information content of the MICA of the two concepts) has also been tuned to take into account the fact that concepts can have multiple parents [33], which is necessary in many life science fields since it is in the nature of biomedical ontologies that some concepts are categorised under multiple parents, (see
The way to measure shared information content has also been completely re-implemented to use not the IC of the most informative common ancestor but a metric based on the set of all ancestors of the concepts [36].
These measures are able to compare one concept with another. It is also possible to compare sets of concepts. For this, one takes the matrix of pairwise similarities between concepts in the first set and concepts in the second set and mathematically manipulates it to produce a single number, taking, for example, the average, the maximum, or the “best match average”, an approach that averages the highest values in each row and column [22]. There are other approaches that convert a set of concepts into the set of all their ancestors and take the intersection of those sets as a measure of similarity (two examples are simUI and simGIC [22]).
Finally, there is a difference in measuring the similarity or the relatedness between concepts. Similarity is a term that is generally applied to the notion that two concepts are “alike” and is usually computed based on “is-a” hierarchies; relatedness is more general: two related concepts can be related based on their categorisation on a hierarchy or on any number of other non-hierarchical relations. This distinction is important in chemistry, and ChEBI in particular, since many chemistry concepts are related via relations such as “has-role”, “has-part”, “is-enantiomer-of”, etc.
Notice that when nothing is known about a chemical compound other than its structure, semantic methods can still be used, because one of the ways ontologies (especially ChEBI) classify molecules is based on their structure. For example, ChEBI has a concept “carboxylic acid” which is an ancestor of all molecules that have one or more carboxylic acid groups (e.g., benzoic acid, all amino acids, all penicillins, etc.). This, however, is not conceptually different from measuring structural similarity, and such a setting would lack the enrichment provided by other types of knowledge (e.g., the knowledge of the chemical and biological roles of the molecule).
Since 2003, when Lord et al. [28] introduced the idea of ontology-based semantic similarity in the gene ontology (GO), several results have been achieved using this technique, proving beyond doubt that it is sound and useful and has real-life applications. In genomics and proteomics, semantic similarity based on GO has been used to (i) cluster proteins [37], (ii) find protein-protein interactions [38], (iii) interpret microarray data [39], (iv) predict protein functions [40], (v) prioritise candidate disease genes [41], etc. Other uses outside GO include predicting disease-related phenotypes [42] and predicting clinical diagnosis from a set of phenotype abnormalities [43].
The uses in chemistry-related areas have been scarce, but nonetheless existing and with real-world applications. We collected three research studies of semantic similarity in cheminformatics, which show its use in this area.
In 2010, ontology-based semantic similarity was applied to ChEBI [44] using a methodology named Chym. Chym shows for the first time that semantic similarity is useful in biomedical chemistry, by applying these ideas to predict whether a molecule (i) is capable of crossing the blood brain barrier, (ii) is a substrate of the P-glycoprotein, and (iii) binds to an oestrogen receptor. These properties are at least partially intrinsically related to the three-dimensional structure of the molecules and also of the proteins that perform the biochemical role in the organism. However, the work shows that structural similarity alone can be improved if it is coupled with semantic similarity.
Chym used daylight fingerprints for structural similarity and simUI and simGIC for semantic similarity, using ChEBI as the ontology. For all the three properties mentioned above, Chym was able to clearly outperform what were then the state-of-the-art prediction techniques for those properties.
Notice that this means that the two ideas presented here, structural similarity and semantic similarity, are not orthogonal and can be applied simultaneously with good results. This is not surprising, as ontologies can complement the knowledge that can be inferred form the structure alone, without needing to resort to wet-lab experiments.
As the amount of textual chemistry information increases, particularly in the form of drug leaflets, articles, patents, and other types of communications, the need to develop mechanisms to automatically read these texts and extract tractable information from them increases as well. In this context, named entity recognition is a text mining task whose goal is to identify the entities mentioned in text.
There have been many attempts to create such systems in the chemical domain (see, e.g., the review [45]). In one of those attempts [46], semantic similarity has been used to improve the precision of existing methodologies by successfully identifying some false positives and removing them from the final result set. The idea of that work is that, within a scope of text (e.g., a sentence or a paragraph), chemical entities mentioned in that scope share some degree of semantic similarity that is higher than average. When entity recognition algorithms offer more than one possible ChEBI identifier for an excerpt of text, similarity with other ChEBI concepts can be used to disambiguate which is the correct entity.
Drug repurposing is the process by which drug that have therapeutic application are computationally tested to find other therapeutic applications. This reduces costs and improves the drug development pipeline and as such is important for the pharmaceutical industry.
The work presented in [47] couples similarity between the three-dimensional molecular structure with semantic similarity between the drug targets to find new indications for known drugs. The ontology used here is not a chemistry-specific one, but GO.
The main methodology of this work was:
Select a drug d and a potential target protein p.
Find drugs similar to this one (up to a threshold) with a structural similarity measure. Store these structural similarity values in a vector
For each similar drug di, find its interacting proteins, compare them with p using GO-based semantic similarity, and sum the results. Call this value pi. We have now a vector
The drug-protein association is assigned a score that depends on the correlation between the vectors Xstr and Xsem. For a set of N proteins, each drug was then assigned a vector of N drug-protein association values, called the drug’s “expression profile”.
The drug-drug similarity measure was computed based on the correlation between the “expression profiles” of the two drugs.
The similarity between drugs was then used to construct a network of similarities, where clusters of highly connected drugs were indicative of potential drug repurposing.
A related work [48] also uses semantic similarity to predict drug-protein interaction. In this work, probabilistic similarity logic is used to construct models that are based on a notion of “similarity triads”: triples of the form “drug-target-drug” with similar drugs or “target-drug-target” with similar targets. The whole work was based on the assumption that similar targets tend to interact with the same drug and similar drugs tend to interact with the same target. Here, several protein similarity methods (including semantic similarity based on GO) and drug similarity method (including semantic similarity based on ATC) were used to build a probabilistic model that predicts whether drugs and proteins interact.
Semantic similarity in cheminformatics has been slow to keep with the pace of equivalent research in other life science fields, such as genomics and proteomics. We posit that this is in some ways related to general and specific challenges associated with the application of this methodology in chemistry.
First, the state of ontology development and the more general knowledge representation area is very active, specifically in the biomedical fields. This means that many people have the motivation to develop their own ontology, with specific views of the reality embedded in it. However, as many people create their own knowledge representation artefacts, many different ontologies start to appear that overlap in domain, which means that it is not always obvious which ontology (or ontologies) to choose for a specific goal. Furthermore, these ontologies are not easy to reconcile, because they encode different and disjoint points of view. While efforts have been made to attenuate this problem, such as ontology matching (the process by which ontologies of the same domain are automatically merged into a single ontology) and the establishment of community standards (in chemistry, e.g., it is standard practice to reuse ChEBI concepts rather than create new concepts in new ontologies), the problem still persists.
Second, metrics of semantic similarity have been mostly developed and tested in the fields of natural language processing and genomics/proteomics. While these seem to have good enough results when used with ChEBI, we still do not know if they are the most adequate measures in this domain. Ferreira et al. [34] developed and validated a measure on the chemical domain, but more work needs to be done in this area. In particular, what role should the non-hierarchical relationship types (“is-enantiomer-of”, “is-conjugate-acid-of”, etc.) have in semantic similarity?
The third challenge is one of similarity profiles. It is not always obvious which details or properties of a molecule should be used for comparing. Should a pair of chemical compounds that differ only in the presence of an oxygen atom (e.g., methane vs. methanol) be more similar than a pair of molecules that differ only in charge (e.g., NO2 vs. NO2−) or only in their three-dimensional conformation (e.g., L-serine vs. D-serine)? This problem must be solved based on context: determining what the similarity measure will be used for and then deciding which features are important. This includes deciding, for example, which relationship types should be taken into account, how to weight them, etc. Maggiora et al. [49] touch on the fact that chemoinformaticians and medicinal chemists typically perceive similarity differently and we need to find ways to capture those differences in actionable measures of similarity.
The fourth challenge is the necessity of taking into account multiple domains of knowledge: drugs interact with proteins, treat and cause diseases, are produced by different methods (industrial or otherwise), have side effects, participate in metabolic reactions, etc. These concepts from other domains can also be compared semantically (many are even already represented in appropriate ontologies, including diseases, proteins, types of molecular interaction, manufacturing procedures, side effects, and pathways). The question now is how to take advantage of these other ontologies in order to implement a useful and accurate measure of chemical similarity. This issue is even related to the previous one, since by tuning the weight of these other domains, we can create new profiles of similarity more pertinent to some goals than others.
Another challenge is the absence of a standardised way to validate the measures that are proposed. In practice, for each new measure being proposed by some research group, that same group validates the new measure by comparing them with previous ones or by using it to show that the new measure can find hidden knowledge in some dataset. However, the ad hoc way these validations are performed means that frequently the measures are neither comparable nor interchangeable and that they can only be used for the goal used to validate them. Thus, a general but useful validation strategy should also be developed to bring cohesion to this field.
This chapter introduces the ideas behind ontology-based semantic similarity measures, how they are applied in life sciences, and some of their uses in chemistry-related research endeavours. The main idea that we exposed is that these methods, having been used in other biomedical fields, can help cheminformatics in several fronts. We described three applications of where this methodology has been applied directly in cheminformatics research efforts and expect that this number grows as more people are exposed to this idea and its use cases.
We also exposed some of the future challenges in this area, which can serve as a starting point to anyone wishing to improve on the work already published, and provided general guidelines that should be taken into account for the further improvement of cheminformatics as a scientific field. In particular, we emphasise the need to explore the multidomain potential in semantic similarity, as well as the need to standardise the ways to validate measures of semantic similarity.
This work was supported by FCT through funding of DeST: Deep Semantic Tagger project, ref. PTDC/CCI-BIO/28685/2017 (
ATC | anatomical therapeutic chemical classification system |
ChEBI | chemical entities of biological interest |
DAG | directed acyclic graph |
GO | gene ontology |
IC | information content |
MeSH | medical subject headings |
MICA | most informative common ancestor |
OBO | Open Biological and Biomedical Ontology |
QSAR | quantitative structure-activity relationship |
simGIC | similarity of graphs with information content |
simUI | similarity with union and intersection |
SMILES | simplified molecular-input line-entry system |
SNOMED CT | systematised nomenclature of medicine—clinical terms |
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