Person Re-identification by Multi-resolution Saliency-Weighted Color Histograms and Local Structural Sparse Coding
Person re-identification plays an important role in computer vision, aiming to identify the same person viewed by disjoint cameras at different time instants and locations. In this paper we present a novel appearance-based method by multi-resolution saliency-weighted color histograms and local structural sparse coding for re-identification work. The former descriptor captures global chromatic content while the latter exploits both partial and spatial information of individuals. Specifically, visual saliency is considered as weighting operators to increase the discriminative power of features. Finally a combinational matching strategy is employed to measure the similarity between individuals. Experimental results over two challenging benchmark datasets (VIPeR, ETHZ) demonstrate that our method obtains competitive performance.