Refined clothing texture parsing by exploiting the discriminative meanings of sparse codes
Fan Wang, Zhao Qiyang, Yin Baolin · 2014
Texture parsing benefits attribute-based clothing analysis and related applications, such as clothing retrieval and recognition. To deal with the large variations of clothing textures, in this paper, a new method is presented in which refined texture attributes are parsed. Based on the characteristics of clothing textures, refined texture attributes are proposed and parameterized. To estimate the attribute parameters, we exploit the discriminative meanings of sparse codes: the underlying connections between the attribute parameters and each component of sparse codes. The attribute parameters are mapped from the dominating components of sparse codes. Our experiments demonstrate the effectiveness of the proposed method.