Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics, etc. However, the conventional sparse coding algorithms and its manifold regularized variants (graph sparse coding and Laplacian sparse coding), learn the codebook and codes in a unsupervised manner and neglect the class informati...

A method, named competitive sparse representation classification (CSRC), is proposed for face recognition in this paper. CSRC introduces a lowest competitive deletion mechanism which removes the lowest competitive sample based on the competitive ability of training samples for representing a probe in multiple rounds collaborative linear representation. In other words, in each round of competing...

In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC). Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA) to alleviate the influence of noises (e.g., illumination difference and o...

The recently proposed l2-norm based collaborative representation for classification (CRC) model has shown inspiring performance on face recognition after the success of its predecessor — the l1-norm based sparse representation for classification (SRC) model. Though CRC is much faster than SRC as it has a closed-form solution, it may have the same weakness as SRC, i.e., relying on a “good” (prop...

In this paper, we introduce an efficient tensor to vector projection algorithm for human gait feature representation and recognition. The proposed approach is based on the multidimensional or tensor signal processing technology, which finds a low-dimensional tensor subspace of original input gait sequence tensors while most of the data variation has been well captured. In order to further enhan...

Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description...

This paper proposes a novel approach for learning discriminative and sparse representations. It consists of utilizing two different models. A predefined number of non-linear transform models are used in the learning stage and one sparsifying transform model is used at test time. The non-linear transform models have discriminative and minimum information loss priors. A novel measure related to t...

This paper proposes a novel approach for learning discriminative and sparse representations. It consists of utilizing two different models. A predefined number of non-linear transform models are used in the learning stage, and one sparsifying transform model is used at test time. The non-linear transform models have discriminative and minimum information loss priors. A novel measure related to ...

This paper proposes a novel approach for learning discriminative and sparse representations. It consists of utilizing two different models. A predefined number of non-linear transform models are used in the learning stage, and one sparsifying transform model is used at test time. The non-linear transform models have discriminative and minimum information loss priors. A novel measure related to ...

The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic en...