Salient Color Names for Person Re-identification

Yang Yang, Jimei Yang, Junjie Yan, Shengcai Liao, Dong Yi, Stan Z. Li

Abstract:

Color naming, which relates colors with color names, can
help people with a semantic analysis of images in many computer vision
applications. In this paper, we propose a novel salient color names based
color descriptor (SCNCD) to describe colors. SCNCD utilizes salient color names to guarantee that a higher probability will be assigned
to the color name which is nearer to the color. Based on SCNCD, color distributions over color names in different color spaces are then obtained and fused to generate a feature representation. Moreover, the
effect of background information is employed and analyzed for person
re-identification. With a simple metric learning method, the proposed
approach outperforms the state-of-the-art performance (without user’s
feedback optimization) on two challenging datasets (VIPeR and PRID
450S). More importantly, the proposed feature can be obtained very fast
if we compute SCNCD of each color in advance.