Multiple metric learning based on bar-shape descriptor for person re-identification

Abstract

The robust structural feature extraction and similarity measure play critical roles in person re-identification. This paper presents a novel algorithm named Multiple Metric Learning based on Bar-shape Descriptor (MMLBD) for person re-identification. Specifically, we first propose a new Multiple Bar-shape Descriptor that can take full account of the spatial correlation between the center points and their adjacent points on different directions. It captures further histogram features based on a novel color difference weight factors with an overlapping sliding window, which can depict the local variations and consistency in the whole image. The similarity and dissimilarity of samples are used to train the weight factor of features and an optimal subspace could be obtained at the same time. Next, we provide an effective multiple metric learning method fusing two-channel bar-shape structural features via the optimal similarity pairwise measure obtained by a dissimilarity matrix. This measure can fully mine the discriminative information and eliminate redundancy in the similar features, which make the MMLBD simple and effective. Finally, evaluation experiments on the i_LIDS, CAVIAR4REID and WARD data-sets are carried out, which compare the proposed MMLBD with the corresponding methods. Experimental results demonstrate that the MMLBD is more effective and robust against visual appearance variations.