Learning component-level sparse representation for image and video categorization

A novel component-level dictionary learning framework that exploits image/video group characteristics based on sparse representation is introduced in this paper. Unlike the previous methods that select the dictionaries to best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and component-level importance within one unified framework to provide a discriminative and sparse representation for image/video groups. The importance measures how well each feature component represents the group property with the dictionary. Then, the dictionary is updated iteratively to reduce the influence of unimportant components, thus refining the sparse representation for each group. In the end, by keeping the top K important components, a compact representation is obtained for the sparse coding dictionary. Experimental results on several public image and video data sets are shown to demonstrate the superior performance of the proposed algorithm compared with the-state-of-the-art methods.