Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set

To solve the problem of inaccurate feature representation caused by indistinctive appearance difference in person re-identification domain, a new Matrix Metric Learning algerithm based on Bidirectional Reference ((BRML)-L-2) set is proposed. Firstly, reciprocal-neighbor reference sets in different camera views are respectively constructed by the reciprocal-neighbor scheme. To ensure the robustness of reference sets, the reference sets in different camera views are jointly considered to generate the Bidirectional Reference Set (BRS). With hard samples which are mined by the BRS to represent feature descriptors, accurate appearance difference representations could be obtained. Finally, these representations are utilized to conduct more effective matrix metric learning. Experimental results on several public datasets demonstrate the superiority of the proposed method.