Saliency-based Deep Multi-level Semantic Feature Fusion for Person Re-identification

Abstract

Person re-identification (Re-ID) is a challenging problem due to external environmental disturbances and significant intra-class appearance variations. Discriminative person features should cover global and partial representations, and accordingly can describe high-level and middle-level semantic information of persons. To achieve this goal, a saliency-based deep multi-level semantic feature representation and fusion algorithm is proposed. Firstly, a feature fusion scheme at middle layer of our deep network is presented to effectively fuse global CNN features. Secondly, a part-based feature extraction method is designed to extract high-level semantic features. In addition, a parameter-free multi-scale saliency-based enhancement algorithm is proposed to compute patch-level saliency scores to enhance the distinctiveness of part-based features. Experimental results on three public datasets, namely, Market-1501, DukeMTMC-reID, and CHUK03, demonstrate the effectiveness of the proposed method compared with state-of-the-art Re-ID approaches.