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

SPARSE REPRESENTATION, DISCRIMINATIVE DICTIONARIES AND PROJECTIONS FOR VISUAL CLASSIFICATION

Developments in sensing and communication technologies have led to an explosion in the availability of visual data from multiple sources and modalities. Millions of cameras have been installed in buildings, streets, and airports around the world that are capable of capturing multimodal information such as light, depth, heat etc. These data are potentially a tremendous resource for building robust visual detectors and classifiers. However, the data are often large, mostly unlabeled and increasingly of mixed modality. To extract useful information from these heterogeneous data, one needs to exploit the underlying physical, geometrical or statistical structure across data modalities. For instance, in computer vision, the number of pixels in an image can be rather large, but most inference or representation models use only a few parameters to describe the appearance, geometry, and dynamics of a scene. This has motivated researchers to develop a number of techniques for finding a low-dimensional representation of a high-dimensional dataset. The dominant methodology for modeling and exploiting the low-dimensional structure in high dimensional data is sparse dictionary-based modeling. While discriminative dictionary learning have demonstrated tremendous success in computer vision applications, their performance is often limited by the amount and type of labeled data available for training. In this dissertation, we extend the sparse dictionary learning framework for weakly supervised learning problems such as semi-supervised learning, ambiguously labeled learning and Multiple Instance Learning (MIL). Furthermore, we present nonlinear extensions of these methods using the kernel trick. We also address the problem of choosing the optimal kernel for sparse representation-based classification using Multiple Kernel Learning (MKL) methods. Finally, in order to deal with heterogeneous multimodal data, we present a feature level fusion method based on quadratic programing. The dissertation has been divided into following four parts: 1) In the first part, we develop a discriminative non-linear dictionary learning technique which utilizes both labeled and unlabeled data for learning dictionaries. We compute a probability distribution over class labels for all the unlabeled samples which is updated together with dictionary and sparse coefficients. The algorithm is also extended for ambiguously labeled data when part of the data contains multiple labels for a training sample. 2) Using non-linear dictionaries, we present a multi-class Multiple Instance Learning (MIL) algorithm where the data is given in the form of bags. Each bag contains multiple samples, called instances, out of which at least one belongs to the class of the bag. We propose a noisy-OR model and a generalized mean-based optimization framework for learning the dictionaries in the feature space. The proposed method can be viewed as a generalized dictionary learning algorithm since it reduces to a novel discriminative dictionary learning framework when there is only one instance in each bag. 3) We propose a Multiple Kernel Learning (MKL) algorithm that is based on the Sparse Representation-based Classification (SRC) method. Taking advantage of the non-linear kernel SRC in efficiently representing the non-linearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and the sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. 4) Finally, using a linear classification model, we study the problem of fusing information from multiple modalities. Many current recognition algorithms combine different modalities based on training accuracy but do not consider the possibility of noise at test time. We describe an algorithm that perturbs test features so that all modalities predict the same class. We enforce this perturbation to be as small as possible via a quadratic program (QP) for continuous features, and a mixed integer program (MIP) for binary features. To efficiently solve the MIP, we provide a greedy algorithm and empirically show that its solution is very close to that of a state-of-the-art MIP solver

Partial Label Learning with Self-Guided Retraining

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.Comment: 8 pages, accepted by AAAI-1

Decomposition-based Generation Process for Instance-Dependent Partial Label Learning

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way. However, these approaches usually do not perform as well as expected due to the fact that the generation process of the candidate labels is always instance-dependent. Therefore, it deserves to be modeled in a refined way. In this paper, we consider instance-dependent PLL and assume that the generation process of the candidate labels could decompose into two sequential parts, where the correct label emerges first in the mind of the annotator but then the incorrect labels related to the feature are also selected with the correct label as candidate labels due to uncertainty of labeling. Motivated by this consideration, we propose a novel PLL method that performs Maximum A Posterior(MAP) based on an explicitly modeled generation process of candidate labels via decomposed probability distribution models. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method