In this paper, we propose an efficient sparse feature on-line learning approach for image classification. A large-margin formulation solved by linear programming is adopted to learn sparse features on the max-similarity based image representation. The margins between the training images and the query images can be directly utilized for classification by the Naive-Bayes or the K Nearest Neighbor...

With the rapid development of surveillance technology, there are often several cameras in one scenario. The multi-camera usage to perform gait recognition becomes a challenge problem. This paper studies multi-camera gait recognition via structure sparsity. For the multicamera structure in the training set, we propose a structure sparsity algorithm to learn informative and discriminative sparse ...

We proposes a hierarchical feature extraction method, the sparse neural response, motivated by the neuroscience of the visual cortex. The proposed method builds an increasingly complex image representation by alternating between a sparse coding and a maximum pooling operation. Generally speaking, each sample (image patch or low layer sparse neural response) and its neighbors lie on or close to ...

Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE). BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation (BLSR) and block-diagonal constrained low-rank and sparse ...

Dictionary learning (DL) for sparse coding based classification has been widely researched in pattern recognition in recent years. Most of the DL approaches focused on the reconstruction performance and the discriminative capability of the learned dictionary. This paper proposes a new method for learning discriminative dictionary for sparse representation based classification, called Incoherent...

Dense local trajectories have been successfully used in action recognition. However, for most actions only a few local motion features (e.g., critical movement of hand, arm, leg etc.) are responsible for the action label. Therefore, highlighting the local features which are associated with important motion parts will lead to a more discriminative action representation. Inspired by recent advanc...