Pattern vectors from algebraic graph theory

Graphstructures have proven computationally cumbersome for pattern analysis. The reason for this is that, before graphs can be converted to pattern vectors, correspondences must be established between the nodes of structures which are potentially of different size. To overcome this problem, in this paper, we turn to the spectral decomposition of the Laplacian matrix. We show how the elements of the spectral matrix for the Laplacian can be used to construct symmetric polynomials that are permutation invariants. The coefficients of these polynomials can be used as graph features which can be encoded in a vectorial manner. We extend this representation to graphs in which there are unary attributes on the nodes and binary attributes on the edges by using the spectral decomposition of a Hermitian property matrix that can be viewed as a complex analogue of the Laplacian. To embed the graphs in a pattern space, we explore whether the vectors of invariants can be embedded in a low- dimensional space using a number of alternative strategies, including principal components analysis ( PCA), multidimensional scaling ( MDS), and locality preserving projection ( LPP). Experimentally, we demonstrate that the embeddings result in well- defined graph clusters. Our experiments with the spectral representation involve both synthetic and real- world data. The experiments with synthetic data demonstrate that the distances between spectral feature vectors can be used to discriminate between graphs on the basis of their structure. The real- world experiments show that the method can be used to locate clusters of graphs

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Large margin methods for partner specific prediction of interfaces in protein complexes

2014 Spring.The study of protein interfaces and binding sites is a very important domain of research in bioinformatics. Information about the interfaces between proteins can be used not only in understanding protein function but can also be directly employed in drug design and protein engineering. However, the experimental determination of protein interfaces is cumbersome, expensive and not possible in some cases with today's technology. As a consequence, the computational prediction of protein interfaces from sequence and structure has emerged as a very active research area. A number of machine learning based techniques have been proposed for the solution to this problem. However, the prediction accuracy of most such schemes is very low. In this dissertation we present large-margin classification approaches that have been designed to directly model different aspects of protein complex formation as well as the characteristics of available data. Most existing machine learning techniques for this task are partner-independent in nature, i.e., they ignore the fact that the binding propensity of a protein to bind to another protein is dependent upon characteristics of residues in both proteins. We have developed a pairwise support vector machine classifier called PAIRpred to predict protein interfaces in a partner-specific fashion. Due to its more detailed model of the problem, PAIRpred offers state of the art accuracy in predicting both binding sites at the protein level as well as inter-protein residue contacts at the complex level. PAIRpred uses sequence and structure conservation, local structural similarity and surface geometry, residue solvent exposure and template based features derived from the unbound structures of proteins forming a protein complex. We have investigated the impact of explicitly modeling the inter-dependencies between residues that are imposed by the overall structure of a protein during the formation of a protein complex through transductive and semi-supervised learning models. We also present a novel multiple instance learning scheme called MI-1 that explicitly models imprecision in sequence-level annotations of binding sites in proteins that bind calmodulin to achieve state of the art prediction accuracy for this task

REPRESENTATION LEARNING FOR ACTION RECOGNITION

The objective of this research work is to develop discriminative representations for human actions. The motivation stems from the fact that there are many issues encountered while capturing actions in videos like intra-action variations (due to actors, viewpoints, and duration), inter-action similarity, background motion, and occlusion of actors. Hence, obtaining a representation which can address all the variations in the same action while maintaining discrimination with other actions is a challenging task. In literature, actions have been represented either using either low-level or high-level features. Low-level features describe the motion and appearance in small spatio-temporal volumes extracted from a video. Due to the limited space-time volume used for extracting low-level features, they are not able to account for viewpoint and actor variations or variable length actions. On the other hand, high-level features handle variations in actors, viewpoints, and duration but the resulting representation is often high-dimensional which introduces the curse of dimensionality. In this thesis, we propose new representations for describing actions by combining the advantages of both low-level and high-level features. Specifically, we investigate various linear and non-linear decomposition techniques to extract meaningful attributes in both high-level and low-level features. In the first approach, the sparsity of high-level feature descriptors is leveraged to build action-specific dictionaries. Each dictionary retains only the discriminative information for a particular action and hence reduces inter-action similarity. Then, a sparsity-based classification method is proposed to classify the low-rank representation of clips obtained using these dictionaries. We show that this representation based on dictionary learning improves the classification performance across actions. Also, a few of the actions consist of rapid body deformations that hinder the extraction of local features from body movements. Hence, we propose to use a dictionary which is trained on convolutional neural network (CNN) features of the human body in various poses to reliably identify actors from the background. Particularly, we demonstrate the efficacy of sparse representation in the identification of the human body under rapid and substantial deformation. In the first two approaches, sparsity-based representation is developed to improve discriminability using class-specific dictionaries that utilize action labels. However, developing an unsupervised representation of actions is more beneficial as it can be used to both recognize similar actions and localize actions. We propose to exploit inter-action similarity to train a universal attribute model (UAM) in order to learn action attributes (common and distinct) implicitly across all the actions. Using maximum aposteriori (MAP) adaptation, a high-dimensional super action-vector (SAV) for each clip is extracted. As this SAV contains redundant attributes of all other actions, we use factor analysis to extract a novel lowvi dimensional action-vector representation for each clip. Action-vectors are shown to suppress background motion and highlight actions of interest in both trimmed and untrimmed clips that contributes to action recognition without the help of any classifiers. It is observed during our experiments that action-vector cannot effectively discriminate between actions which are visually similar to each other. Hence, we subject action-vectors to supervised linear embedding using linear discriminant analysis (LDA) and probabilistic LDA (PLDA) to enforce discrimination. Particularly, we show that leveraging complimentary information across action-vectors using different local features followed by discriminative embedding provides the best classification performance. Further, we explore non-linear embedding of action-vectors using Siamese networks especially for fine-grained action recognition. A visualization of the hidden layer output in Siamese networks shows its ability to effectively separate visually similar actions. This leads to better classification performance than linear embedding on fine-grained action recognition. All of the above approaches are presented on large unconstrained datasets with hundreds of examples per action. However, actions in surveillance videos like snatch thefts are difficult to model because of the diverse variety of scenarios in which they occur and very few labeled examples. Hence, we propose to utilize the universal attribute model (UAM) trained on large action datasets to represent such actions. Specifically, we show that there are similarities between certain actions in the large datasets with snatch thefts which help in extracting a representation for snatch thefts using the attributes from the UAM. This representation is shown to be effective in distinguishing snatch thefts from regular actions with high accuracy.In summary, this thesis proposes both supervised and unsupervised approaches for representing actions which provide better discrimination than existing representations. The first approach presents a dictionary learning based sparse representation for effective discrimination of actions. Also, we propose a sparse representation for the human body based on dictionaries in order to recognize actions with rapid body deformations. In the next approach, a low-dimensional representation called action-vector for unsupervised action recognition is presented. Further, linear and non-linear embedding of action-vectors is proposed for addressing inter-action similarity and fine-grained action recognition, respectively. Finally, we propose a representation for locating snatch thefts among thousands of regular interactions in surveillance videos

Prediction of protein binding sites in protein structures using hidden Markov support vector machine

<p>Abstract</p> <p>Background</p> <p>Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.</p> <p>Results</p> <p>In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.</p> <p>Conclusion</p> <p>The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.</p

Novel algorithms for 3D human face recognition

textAutomated human face recognition is a computer vision problem of considerable practical significance. Existing two dimensional (2D) face recognition techniques perform poorly for faces with uncontrolled poses, lighting and facial expressions. Face recognition technology based on three dimensional (3D) facial models is now emerging. Geometric facial models can be easily corrected for pose variations. They are illumination invariant, and provide structural information about the facial surface. Algorithms for 3D face recognition exist, however the area is far from being a matured technology. In this dissertation we address a number of open questions in the area of 3D human face recognition. Firstly, we make available to qualified researchers in the field, at no cost, a large Texas 3D Face Recognition Database, which was acquired as a part of this research work. This database contains 1149 2D and 3D images of 118 subjects. We also provide 25 manually located facial fiducial points on each face in this database. Our next contribution is the development of a completely automatic novel 3D face recognition algorithm, which employs discriminatory anthropometric distances between carefully selected local facial features. This algorithm neither uses general purpose pattern recognition approaches, nor does it directly extend 2D face recognition techniques to the 3D domain. Instead, it is based on an understanding of the structurally diverse characteristics of human faces, which we isolate from the scientific discipline of facial anthropometry. We demonstrate the effectiveness and superior performance of the proposed algorithm, relative to existing benchmark 3D face recognition algorithms. A related contribution is the development of highly accurate and reliable 2D+3D algorithms for automatically detecting 10 anthropometric facial fiducial points. While developing these algorithms, we identify unique structural/textural properties associated with the facial fiducial points. Furthermore, unlike previous algorithms for detecting facial fiducial points, we systematically evaluate our algorithms against manually located facial fiducial points on a large database of images. Our third contribution is the development of an effective algorithm for computing the structural dissimilarity of 3D facial surfaces, which uses a recently developed image similarity index called the complex-wavelet structural similarity index. This algorithm is unique in that unlike existing approaches, it does not require that the facial surfaces be finely registered before they are compared. Furthermore, it is nearly an order of magnitude more accurate than existing facial surface matching based approaches. Finally, we propose a simple method to combine the two new 3D face recognition algorithms that we developed, resulting in a 3D face recognition algorithm that is competitive with the existing state-of-the-art algorithms.Electrical and Computer Engineerin

Face Recognition: Study and Comparison of PCA and EBGM Algorithms

Face recognition is a complex and difficult process due to various factors such as variability of illumination, occlusion, face specific characteristics like hair, glasses, beard, etc., and other similar problems affecting computer vision problems. Using a system that offers robust and consistent results for face recognition, various applications such as identification for law enforcement, secure system access, computer human interaction, etc., can be automated successfully. Different methods exist to solve the face recognition problem. Principal component analysis, Independent component analysis, and linear discriminant analysis are few other statistical techniques that are commonly used in solving the face recognition problem. Genetic algorithm, elastic bunch graph matching, artificial neural network, etc. are few of the techniques that have been proposed and implemented. The objective of this thesis paper is to provide insight into different methods available for face recognition, and explore methods that provided an efficient and feasible solution. Factors affecting the result of face recognition and the preprocessing steps that eliminate such abnormalities are also discussed briefly. Principal Component Analysis (PCA) is the most efficient and reliable method known for at least past eight years. Elastic bunch graph matching (EBGM) technique is one of the promising techniques that we studied in this thesis work. We also found better results with EBGM method than PCA in the current thesis paper. We recommend use of a hybrid technique involving the EBGM algorithm to obtain better results. Though, the EBGM method took a long time to train and generate distance measures for the given gallery images compared to PCA. But, we obtained better cumulative match score (CMS) results for the EBGM in comparison to the PCA method. Other promising techniques that can be explored separately in other paper include Genetic algorithm based methods, Mixture of principal components, and Gabor wavelet techniques

Automatic Person Verification Using Speech and Face Information

Interest in biometric based identification and verification systems has increased considerably over the last decade. As an example, the shortcomings of security systems based on passwords can be addressed through the supplemental use of biometric systems based on speech signals, face images or fingerprints. Biometric recognition can also be applied to other areas, such as passport control (immigration checkpoints), forensic work (to determine whether a biometric sample belongs to a suspect) and law enforcement applications (e.g. surveillance). While biometric systems based on face images and/or speech signals can be useful, their performance can degrade in the presence of challenging conditions. In face based systems this can be in the form of a change in the illumination direction and/or face pose variations. Multi-modal systems use more than one biometric at the same time. This is done for two main reasons -- to achieve better robustness and to increase discrimination power. This thesis reviews relevant backgrounds in speech and face processing, as well as information fusion. It reports research aimed at increasing the robustness of single- and multi-modal biometric identity verification systems. In particular, it addresses the illumination and pose variation problems in face recognition, as well as the challenge of effectively fusing information from multiple modalities under non-ideal conditions