Cross-view semantic projection learning for person re-identification

Feature transformation is of great importance to strengthen the descriptive power of feature representation for many classification and recognition tasks. In this paper, we propose a novel cross-view semantic projection learning method for extracting latent semantics from the hand-crafted features. Specifically, the shared latent basis matrix, the view-specific semantic projection functions and the optimal associations of different views are jointly learned in a unified matrix factorization framework, to get a common semantic space where images of the same person can be well characterized. We further present a generalization of the approach to multiple views. Extensive experiments on a series of challenging datasets highlight the superiorities of the proposed algorithm and demonstrate the effectiveness of the generalized version in multi-view person re-identification applications.