MULTI-SIMILARITY RE-RANKING FOR PERSON RE-IDENTIFICATION

Re-ranking has been proved an effective method to boost the performance of person re-identification. Existing works focus on contextual or graph-based similarity to improve the initial ranking result. The former mainly concentrates on more accurate similarity description but neglects the manifold constraint. While, the later centers on solving similarities with manifold constraint, which acquires several accurate top ranks. In this paper, we propose a novel method which not only takes contextual similarity to generate top ranks accurately but also refines the ranks based on graph-based similarity. Specifically, given initial Euclidean distances between a probe and galleries, we mine contextual and graph-based similarities respectively and then re-rank all galleries with a diffusion procedure under constraints of both similarities. Experiments on two person re-ID datasets demonstrate that our method outperforms state-of-the-art re-ranking approaches in person re-identification.