Saliency-Weighted Global-Local Fusion for Person Re-identification

Many features have been proposed to improve the accuracy of person re-identification. Due to the illumination and viewpoint changes between different cameras, individual feature is less discriminative to separate different persons. In this paper, we propose a saliency-weighted feature descriptor and global-local fusion optimization for person re-identification. Firstly, the weights on pixels are calculated via saliency detection method, then the computed weights are integrated into local maximal occurrence (LOMO) feature descriptor. Secondly, the saliency weights are used to update the metric learning distance in training so that we can learn a new metric matrix for testing. And then, the whole person image is divided into upper and lower halves. A novel global-local fusion method is proposed to combine local and global regions together in the most appropriate way. After that an optimization algorithm is proposed to learn the weights among upper half, lower half and the whole image. According to those weights, a final fused distance is obtained. Experimental results show that the proposed method outperforms many state-of-the-art person re-identification methods.