Jointly Learning Commonality and Specificity Dictionaries for Person Re-Identification
Huafeng Li, Jiajia Xu, Zhengtao Yu, Jiebo Luo
Abstract
Despite advances in person re-identification (re-ID), it is still far from meeting the needs of real-world applications due to tremendous visual ambiguity in person appearance across cameras. To overcome this problem, we propose a person re-ID method by decomposing a pedestrian's appearance feature into different components. This method assumes that each pedestrian image is composed of person-shared components that reflect the similarities of different pedestrians and person-specific components that reflect unique identity information. Based on this assumption, we propose to reduce the ambiguity in visual features by removing person-shared components from pedestrian visual features. To this end, we develop a framework for learning a pair of commonality and specificity dictionaries, while introducing a distance constraint to force the particularities of the same person over the specificity dictionary to have the same coding coefficients and the coding coefficients of different pedestrians to have weak correlation. Furthermore, considering the similarity of the commonality dictionary and the sparsity of the specificity dictionary, low-rank and sparse regularization terms are introduced into the dictionary learning framework to improve their representation ability and discriminative ability. Extensive experimental results show that the proposed algorithm outperforms or is competitive with the state-of-the-art methods.