MULTIPLE DICTIONARY FOR SPARSE MODELING

Much of the progress made in image processing in the past decades can be attributed to better modeling of image content, and a wise deployment of these models in relevant applications. In this paper, we review the role of this recent model in image processing, its rationale, and models related to it. As it turns out, the field of image processing is one of the main beneficiaries from the recent progress made in the theory and practice of sparse and redundant representations. Sparse coding is a key principle that underlies wavelet representation of images. Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. In general, the choice of a proper dictionary can be done using one of two ways: i) building asparsifying  dictionary based on a mathematical model of the data, or ii) learning a dictionary to perform best on a training set

Online reconstruction-free single-pixel image classification

In single-pixel imaging, a series of illumination patterns are projected onto an object and the reflected or transmitted light from the object is integrated by a photodetector (the single-pixel detector). Then, from the set of received photodetector signals, the image of the object can ultimately be reconstructed. However, this reconstruction is not only computationally expensive, but also unnecessary for purposes such as image classification tasks. This work proposes a reconstruction-free multi-class image classification framework that, unlike most of the existing approaches, exploits the sequential nature of the problem. Indeed, by accumulating evidence of the sequence of scalar values, a decision is made after each measurement on whether already classifying the object being imaged, or waiting for more measurements. This online decision relies on a mechanism to achieve a recognition-delay trade-off that induces behaviours within the conservative-to-aggressive spectrum, which suit distinct requirements in different applications. Additionally, the presentation order of the illumination patterns makes a difference in terms of the reconstruction quality (if required) and classification performance when a limited number of patterns is used. Nevertheless, in many cases, simple data- and task-agnostic orders, such as random or frequency-based orders, are commonly used. To address this, a novel sparse-representation-based strategy is presented that sorts the patterns according to their general and discriminability utilities. Both, the online classification framework including the recognition-delay trade-off mechanism, and the data- and task-aware pattern ordering proposed, are experimentally assessed, with encouraging results, on the MNIST digits and CalTech 101 Silhouettes datasets

Collaborative Representation based Classification for Face Recognition

By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored. In this paper we discuss how SRC works, and show that the collaborative representation mechanism used in SRC is much more crucial to its success of face classification. The SRC is a special case of collaborative representation based classification (CRC), which has various instantiations by applying different norms to the coding residual and coding coefficient. More specifically, the l1 or l2 norm characterization of coding residual is related to the robustness of CRC to outlier facial pixels, while the l1 or l2 norm characterization of coding coefficient is related to the degree of discrimination of facial features. Extensive experiments were conducted to verify the face recognition accuracy and efficiency of CRC with different instantiations.Comment: It is a substantial revision of a previous conference paper (L. Zhang, M. Yang, et al. "Sparse Representation or Collaborative Representation: Which Helps Face Recognition?" in ICCV 2011