Learning a multi-class discriminative dictionary with nonredundancy constraints for visual classification

Recent studies have demonstrated advantages of sparse representation in providing an appealing paradigm for visual classification tasks. However, how to effectively learn a compact dictionary of superior reconstruction and discrimination power is still a challenging problem. In this paper, we concurrently exploit both the intraclass and the inter-class visual correlations to learn a multi-class discriminative dictionary. The intra-nonredundancy constraint prevents zero entities from appearing in the class-specific bases, thereby making the learned dictionary more stable. The inter-nonredundancy constraint effectively separates the common visual patterns from all the class-specific bases, yielding a more compact dictionary. Combining nonredundancy constraints with the reconstruction error and the classification error to form a unified objective function, our method can learn a superior dictionary and an optimal linear classifier simultaneously. Extensive experimental results demonstrate that the proposed algorithm achieves notable improvement over the state-of-the-art methods in image classification and visual tracking tasks.