Clothing attributes, of which color plays an important role, are receiving more and more interests in machine vision researches and applications because of their uses and effectiveness in tasks like pedestrian analysis. However, color description is a challenging problem due to complex environments such as illumination variations. Most prior works describe color attributes using only low-level features or mid-level descriptors, which results in a marked drop of the discriminative power or photometric invariance. In this paper we introduce a new efficient joint representation that aims to overcome the shortcomings of using low-level features or mid-level descriptors alone and present a novel hybrid approach to pedestrian clothing color attribute extraction. As a necessary preprocessing step, a novel processing pipeline is also proposed. We evaluate our approach on the task of color classification on both the public dataset VIPeR and our own newly-built pedestrian dataset. Experimental results have demonstrated the effectiveness of our approach and have shown its great potential for further researches and applications.
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