Person Re-Identification by Cross-View Multi-Level Dictionary Learning

Person re-identification plays an important role in many safety-critical applications. Existing works mainly focus on extracting patch-level features or learning distance metrics. However, the representation power of extracted features might be limited, due to the various viewing conditions of pedestrian images in complex real-world scenarios. To improve the representation power of features, we learn discriminative and robust representations via dictionary learning in this paper. First, we propose a Cross-view Dictionary Learning (CDL) model, which is a general solution to the multi-view learning problem. Inspired by the dictionary learning based domain adaptation, CDL learns a pair of dictionaries from two views. In particular, CDL adopts a projective learning strategy, which is more efficient than the l(1) optimization in traditional dictionary learning. Second, we propose a Cross-view Multi-level Dictionary Learning (CMDL) approach based on CDL. CMDL contains dictionary learning models at different representation levels, including image-level, horizontal part-level, and patch-level. The proposed models take advantages of the view-consistency information, and adaptively learn pairs of dictionaries to generate robust and compact representations for pedestrian images. Third, we incorporate a discriminative regularization term to CMDL, and propose a CMDL-Dis approach which learns pairs of discriminative dictionaries in image-level and part-level. We devise efficient optimization algorithms to solve the proposed models. Finally, a fusion strategy is utilized to generate the similarity scores for test images. Experiments on the public VIPeR, CUHKCampus, iLIDS, GRID and PRID450S datasets show that our approach achieves the state-of-the-art performance.