Subspace Representations and Learning for Visual Recognition

Pervasive and affordable sensor and storage technology enables the acquisition of an ever-rising amount of visual data. The ability to extract semantic information by interpreting, indexing and searching visual data is impacting domains such as surveillance, robotics, intelligence, human- computer interaction, navigation, healthcare, and several others. This further stimulates the investigation of automated extraction techniques that are more efficient, and robust against the many sources of noise affecting the already complex visual data, which is carrying the semantic information of interest. We address the problem by designing novel visual data representations, based on learning data subspace decompositions that are invariant against noise, while being informative for the task at hand. We use this guiding principle to tackle several visual recognition problems, including detection and recognition of human interactions from surveillance video, face recognition in unconstrained environments, and domain generalization for object recognition.;By interpreting visual data with a simple additive noise model, we consider the subspaces spanned by the model portion (model subspace) and the noise portion (variation subspace). We observe that decomposing the variation subspace against the model subspace gives rise to the so-called parity subspace. Decomposing the model subspace against the variation subspace instead gives rise to what we name invariant subspace. We extend the use of kernel techniques for the parity subspace. This enables modeling the highly non-linear temporal trajectories describing human behavior, and performing detection and recognition of human interactions. In addition, we introduce supervised low-rank matrix decomposition techniques for learning the invariant subspace for two other tasks. We learn invariant representations for face recognition from grossly corrupted images, and we learn object recognition classifiers that are invariant to the so-called domain bias.;Extensive experiments using the benchmark datasets publicly available for each of the three tasks, show that learning representations based on subspace decompositions invariant to the sources of noise lead to results comparable or better than the state-of-the-art

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

Grassmann Learning for Recognition and Classification

Computational performance associated with high-dimensional data is a common challenge for real-world classification and recognition systems. Subspace learning has received considerable attention as a means of finding an efficient low-dimensional representation that leads to better classification and efficient processing. A Grassmann manifold is a space that promotes smooth surfaces, where points represent subspaces and the relationship between points is defined by a mapping of an orthogonal matrix. Grassmann learning involves embedding high dimensional subspaces and kernelizing the embedding onto a projection space where distance computations can be effectively performed. In this dissertation, Grassmann learning and its benefits towards action classification and face recognition in terms of accuracy and performance are investigated and evaluated. Grassmannian Sparse Representation (GSR) and Grassmannian Spectral Regression (GRASP) are proposed as Grassmann inspired subspace learning algorithms. GSR is a novel subspace learning algorithm that combines the benefits of Grassmann manifolds with sparse representations using least squares loss §¤1-norm minimization for improved classification. GRASP is a novel subspace learning algorithm that leverages the benefits of Grassmann manifolds and Spectral Regression in a framework that supports high discrimination between classes and achieves computational benefits by using manifold modeling and avoiding eigen-decomposition. The effectiveness of GSR and GRASP is demonstrated for computationally intensive classification problems: (a) multi-view action classification using the IXMAS Multi-View dataset, the i3DPost Multi-View dataset, and the WVU Multi-View dataset, (b) 3D action classification using the MSRAction3D dataset and MSRGesture3D dataset, and (c) face recognition using the ATT Face Database, Labeled Faces in the Wild (LFW), and the Extended Yale Face Database B (YALE). Additional contributions include the definition of Motion History Surfaces (MHS) and Motion Depth Surfaces (MDS) as descriptors suitable for activity representations in video sequences and 3D depth sequences. An in-depth analysis of Grassmann metrics is applied on high dimensional data with different levels of noise and data distributions which reveals that standardized Grassmann kernels are favorable over geodesic metrics on a Grassmann manifold. Finally, an extensive performance analysis is made that supports Grassmann subspace learning as an effective approach for classification and recognition

Multi-Modality Human Action Recognition

Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model

DISCRIMINATIVE LEARNING AND RECOGNITION USING DICTIONARIES

In recent years, the theory of sparse representation has emerged as a powerful tool for efficient processing of data in non-traditional ways. This is mainly due to the fact that most signals and images of interest tend to be sparse or compressible in some dictionary. In other words, they can be well approximated by a linear combination of a few elements (also known as atoms) of a dictionary. This dictionary can either be an analytic dictionary composed of wavelets or Fourier basis or it can be directly trained from data. It has been observed that dictionaries learned directly from data provide better representation and hence can improve the performance of many practical applications such as restoration and classification. In this dissertation, we study dictionary learning and recognition under supervised, unsupervised, and semi-supervised settings. In the supervised case, we propose an approach to recognize humans in unconstrained videos, where the main challenge is exploiting the identity information in multiple frames and the accompanying dynamic signature. These identity cues include face, body, and motion. Our approach is based on video-dictionaries for face and body. We design video-dictionaries to implicitly encode temporal, pose, and illumination information. Next, we propose a novel multivariate sparse representation method that jointly represents all the video data by a sparse linear combination of training data. To increase the ability of our algorithm to learn nonlinearities, we apply kernel methods to learn the dictionaries. Next, we address the problem of matching faces across changes in pose in unconstrained videos. Our approach consists of two methods based on 3D rotation and sparse representation that compensate for changes in pose. We demonstrate the superior performance of our approach over several state-of-the-art algorithms through extensive experiments on unconstrained video datasets. In the unsupervised case, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain. The main feature of the proposed approach is that it provides in-plane rotation and scale invariant clustering, which is useful in many applications such as Content Based Image Retrieval (CBIR). We demonstrate through experiments that the proposed rotation and scale invariant clustering provides not only good retrieval performances but also substantial improvements and robustness compared to traditional Gabor-based and several state-of-the-art shape-based methods. We then extend the dictionary learning problem to a generalized semi-supervised formulation, where each training sample is provided with a set of possible labels and only one label among them is the true one. Such applications can be found in image and video collections where one often has only partially labeled data. For instance, given an image with multiple faces and a caption specifying the names, we can be sure that each of the faces belong to one of the names specified, while the exact identity of each face is not known. Labeling involves significant amount of human effort and is expensive. This has motivated researchers to develop learning algorithms from partially labeled training data. In this work, we develop dictionary learning algorithms that utilize such partially labeled data. The proposed method aims to solve the problem of ambiguously labeled multiclass-classification using an iterative algorithm. The dictionaries are updated using either soft (EM-based) or hard decision rules. Extensive evaluations on existing datasets demonstrate that the proposed method performs significantly better than state-of-the-art approaches for learning from ambiguously labeled data. As sparsity plays a major role in our research, we further present a sparse representation-based approach to find the salient views of 3D objects. The salient views are categorized into two groups. The first are boundary representative views that have several visible sides and object surfaces that may be attractive to humans. The second are side representative views that best represent side views of the approximating convex shape. The side representative views are class-specific views and possess the most representative power compared to other within-class views. Using the concept of characteristic view class, we first present a sparse representation-based approach for estimating the boundary representative views. With the estimated boundaries, we determine the side representative views based on a minimum reconstruction error criterion. Furthermore, to evaluate our method, we introduce the notion of geometric dictionaries built from salient views for applications in 3D object recognition, retrieval and sparse-to-full reconstruction. By a series of experiments on four publicly available 3D object datasets, we demonstrate the effectiveness of our approach over state-of-the-art algorithms and baseline methods