Contribution to supervised representation learning: algorithms and applications.

278 p.In this thesis, we focus on supervised learning methods for pattern categorization. In this context, itremains a major challenge to establish efficient relationships between the discriminant properties of theextracted features and the inter-class sparsity structure.Our first attempt to address this problem was to develop a method called "Robust Discriminant Analysiswith Feature Selection and Inter-class Sparsity" (RDA_FSIS). This method performs feature selectionand extraction simultaneously. The targeted projection transformation focuses on the most discriminativeoriginal features while guaranteeing that the extracted (or transformed) features belonging to the sameclass share a common sparse structure, which contributes to small intra-class distances.In a further study on this approach, some improvements have been introduced in terms of theoptimization criterion and the applied optimization process. In fact, we proposed an improved version ofthe original RDA_FSIS called "Enhanced Discriminant Analysis with Class Sparsity using GradientMethod" (EDA_CS). The basic improvement is twofold: on the first hand, in the alternatingoptimization, we update the linear transformation and tune it with the gradient descent method, resultingin a more efficient and less complex solution than the closed form adopted in RDA_FSIS.On the other hand, the method could be used as a fine-tuning technique for many feature extractionmethods. The main feature of this approach lies in the fact that it is a gradient descent based refinementapplied to a closed form solution. This makes it suitable for combining several extraction methods andcan thus improve the performance of the classification process.In accordance with the above methods, we proposed a hybrid linear feature extraction scheme called"feature extraction using gradient descent with hybrid initialization" (FE_GD_HI). This method, basedon a unified criterion, was able to take advantage of several powerful linear discriminant methods. Thelinear transformation is computed using a descent gradient method. The strength of this approach is thatit is generic in the sense that it allows fine tuning of the hybrid solution provided by different methods.Finally, we proposed a new efficient ensemble learning approach that aims to estimate an improved datarepresentation. The proposed method is called "ICS Based Ensemble Learning for Image Classification"(EM_ICS). Instead of using multiple classifiers on the transformed features, we aim to estimate multipleextracted feature subsets. These were obtained by multiple learned linear embeddings. Multiple featuresubsets were used to estimate the transformations, which were ranked using multiple feature selectiontechniques. The derived extracted feature subsets were concatenated into a single data representationvector with strong discriminative properties.Experiments conducted on various benchmark datasets ranging from face images, handwritten digitimages, object images to text datasets showed promising results that outperformed the existing state-ofthe-art and competing methods

DESIGN OF COMPACT AND DISCRIMINATIVE DICTIONARIES

The objective of this research work is to design compact and discriminative dictionaries for e�ective classi�cation. The motivation stems from the fact that dictionaries inherently contain redundant dictionary atoms. This is because the aim of dictionary learning is reconstruction, not classi�cation. In this thesis, we propose methods to obtain minimum number discriminative dictionary atoms for e�ective classi�cation and also reduced computational time. First, we propose a classi�cation scheme where an example is assigned to a class based on the weight assigned to both maximum projection and minimum reconstruction error. Here, the input data is learned by K-SVD dictionary learning which alternates between sparse coding and dictionary update. For sparse coding, orthogonal matching pursuit (OMP) is used and for dictionary update, singular value decomposition is used. This way of classi�cation though e�ective, still there is a scope to improve dictionary learning by removing redundant atoms because our goal is not reconstruction. In order to remove such redundant atoms, we propose two approaches based on information theory to obtain compact discriminative dictionaries. In the �rst approach, we remove redundant atoms from the dictionary while maintaining discriminative information. Speci�cally, we propose a constraint optimization problem which minimizes the mutual information between optimized dictionary and initial dictionary while maximizing mutual information between class labels and optimized dictionary. This helps to determine information loss between before and after the dictionary optimization. To compute information loss, we use Jensen-Shannon diver- gence with adaptive weights to compare class distributions of each dictionary atom. The advantage of Jensen-Shannon divergence is its computational e�ciency rather than calculating information loss from mutual information

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Investigating Social Interactions Using Multi-Modal Nonverbal Features

Every day, humans are involved in social situations and interplays, with the goal of sharing emotions and thoughts, establishing relationships with or acting on other human beings. These interactions are possible thanks to what is called social intelligence, which is the ability to express and recognize social signals produced during the interactions. These signals aid the information exchange and are expressed through verbal and non-verbal behavioral cues, such as facial expressions, gestures, body pose or prosody. Recently, many works have demonstrated that social signals can be captured and analyzed by automatic systems, giving birth to a relatively new research area called social signal processing, which aims at replicating human social intelligence with machines. In this thesis, we explore the use of behavioral cues and computational methods for modeling and understanding social interactions. Concretely, we focus on several behavioral cues in three specic contexts: rst, we analyze the relationship between gaze and leadership in small group interactions. Second, we expand our analysis to face and head gestures in the context of deception detection in dyadic interactions. Finally, we analyze the whole body for group detection in mingling scenarios

OPTIMIZATION ALGORITHMS USING PRIORS IN COMPUTER VISION

Over the years, many computer vision models, some inspired by human behavior, have been developed for various applications. However, only handful of them are popular and widely used. Why? There are two major factors: 1) most of these models do not have any efficient numerical algorithm and hence they are computationally very expensive; 2) many models, being too generic, cannot capitalize on problem specific prior information and thus demand rigorous hyper-parameter tuning. In this dissertation, we design fast and efficient algorithms to leverage application specific priors to solve unsupervised and weakly-supervised problems. Specifically, we focus on developing algorithms to impose structured priors, model priors and label priors during the inference and/or learning of vision models. In many application, it is known a priori that a signal is smooth and continuous in space. The first part of this work is focussed on improving unsupervised learning mechanisms by explicitly imposing these structured priors in an optimization framework using different regularization schemes. This led to the development of fast algorithms for robust recovery of signals from compressed measurements, image denoising and data clustering. Moreover, by employing re-descending robust penalty on the structured regularization terms and applying duality, we reduce our clustering formulation to an optimization of a single continuous objective. This enabled integration of clustering processes in an end-to-end feature learning pipeline. In the second part of our work, we exploit inherent properties of established models to develop efficient solvers for SDP, GAN, and semantic segmentation. We consider models for several different problem classes. a) Certain non-convex models in computer vision (e.g., BQP) are popularly solved using convex SDPs after lifting to a high-dimensional space. However, this computationally expensive approach limits these methods to small matrices. A fast and approximate algorithm is developed that directly solves the original non-convex formulation using biconvex relaxations and known rank information. b) Widely popular adversarial networks are difficult to train as they suffer from instability issues. This is because optimizing adversarial networks corresponds to finding a saddle-point of a loss function. We propose a simple prediction method that enables faster training of various adversarial networks using larger learning rates without any instability problems. c) Semantic segmentation models must learn long-distance contextual information while retaining high spatial resolution at the output. Existing models achieves this at the cost of computationally expensive and memory exhaustive training/inference. We designed stacked u-nets model which can repeatedly process top-down and bottom-up features. Our smallest model exceeds Resnet-101 performance on PASCAL VOC 2012 by 4.5% IoU with ∼ 7× fewer parameters. Next, we address the problem of learning heterogeneous concepts from internet videos using mined label tags. Given a large number of videos each with multiple concepts and labels, the idea is to teach machines to automatically learn these concepts by leveraging weak labels. We formulate this into a co-clustering problem and developed a novel bayesian non-parametric weakly supervised Indian buffet process model which additionally enforces the paired label prior between concepts. In the final part of this work we consider an inverse approach: learning data priors from a given model. Specifically, we develop numerically efficient algorithm for estimating the log likelihood of data samples from GANs. The approximate log-likelihood function is used for outlier detection and data augmentation for training classifiers

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Action recognition from RGB-D data

In recent years, action recognition based on RGB-D data has attracted increasing attention. Different from traditional 2D action recognition, RGB-D data contains extra depth and skeleton modalities. Different modalities have their own characteristics. This thesis presents seven novel methods to take advantages of the three modalities for action recognition. First, effective handcrafted features are designed and frequent pattern mining method is employed to mine the most discriminative, representative and nonredundant features for skeleton-based action recognition. Second, to take advantages of powerful Convolutional Neural Networks (ConvNets), it is proposed to represent spatio-temporal information carried in 3D skeleton sequences in three 2D images by encoding the joint trajectories and their dynamics into color distribution in the images, and ConvNets are adopted to learn the discriminative features for human action recognition. Third, for depth-based action recognition, three strategies of data augmentation are proposed to apply ConvNets to small training datasets. Forth, to take full advantage of the 3D structural information offered in the depth modality and its being insensitive to illumination variations, three simple, compact yet effective images-based representations are proposed and ConvNets are adopted for feature extraction and classification. However, both of previous two methods are sensitive to noise and could not differentiate well fine-grained actions. Fifth, it is proposed to represent a depth map sequence into three pairs of structured dynamic images at body, part and joint levels respectively through bidirectional rank pooling to deal with the issue. The structured dynamic image preserves the spatial-temporal information, enhances the structure information across both body parts/joints and different temporal scales, and takes advantages of ConvNets for action recognition. Sixth, it is proposed to extract and use scene flow for action recognition from RGB and depth data. Last, to exploit the joint information in multi-modal features arising from heterogeneous sources (RGB, depth), it is proposed to cooperatively train a single ConvNet (referred to as c-ConvNet) on both RGB features and depth features, and deeply aggregate the two modalities to achieve robust action recognition