Locality based discriminative measure for multiple-shot person re-identification

Multiple-shot person re-identification tackles the problem to build the correspondences between sets of human images obtained from distributed cameras. It is challenging due to large within-class variations and small between-class differences, caused by the changing of human appearance and environment. Existing methods for addressing this issue include designing the representation to capture the within-set correlation, or crafting the measure to explore the between-set separation. This paper proposes a novel set based matching model called “Locality Based Discriminative Measure (LBDM)”, in which the discriminative potentiality of a new set-to-set distance is exploited by using the learned local metric field. As experimentally demonstrated, the proposal remarkably outperforms state-of-the-art schemes on public benchmark datasets.