Person re-identification by distance metric learning to discrete hashing

Most of the existing works on person re-identification have focused on improving matching rate at top ranks. Few efforts are devoted to address the problem of efficient storage and fast search for person re-identification. In this paper, we investigate the prevailing hashing method, originally designed for large scale image retrieval, for fast person re-identification with efficient storage. We propose a novel hashing approach, namely Distance Metric Learning to Discrete Hashing (DMLDH), which jointly learns a discriminative projection via metric learning to alleviate cross-view variations, and a hashing function for discriminative binary coding by minimizing inner-class Hamming distances and maximizing inter-class Hamming distances. To deal with the formulated non-convex optimization problem, we develop an alternative iteration algorithm by solving several subproblems with analytical solutions. Experimental results on benchmarks demonstrate that the proposed method outperforms the state-of-the-art hashing approaches.