Abstract
Local descriptor based image representation is widely used in biometrics and has achieved promising results. We usually extract the most distinctive local descriptors for image sparse representation due to the large feature space and the redundancy among local descriptors. In this paper, we describe the local descriptor based image representation via a graph model, in which each node is a local descriptor (we call it "atom") and the edges denote the relationship between atoms. Based on this model, a hierarchical structure is constructed to select the most distinctive local descriptors. Two-layer structure is adopted in our work, including local selection and global selection. In the first layer, L