LGSLRR: Towards Fusing Discriminative Ordinal Local and Global Structured Low-Rank Representation for Image Recognition

Structural information extraction has been a focal technique in many classification applications, such as image recognition and biometrics. However, it remains a challenge to simultaneously utilize local and global structural information in a classification model. In addition, in terms of the local information, the existing methods mainly seek to extract or preserve the first-order structure while ignoring the useful ordinal structural information for classification. To this end, this paper presents a discriminative ordinal local and global structured low-rank representation (LGSLRR) model that jointly preserves the local ordinal structure and global structure for image recognition. A discriminative block-diagonal low-rank representation is employed to obtain global information while the first-order and second-order local information is preserved by a joint graph based manifold embedding with two different Laplacian matrices. Some extensive comparison experiments on ten public image datasets are performed, and the results demonstrate the effectiveness and significant performance of the proposed method over some state-of-the-art methods.