Refined clothing texture parsing by exploiting the discriminative meanings of sparse codes

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.

[1]  Yi Yang,et al.  Articulated pose estimation with flexible mixtures-of-parts , 2011, CVPR 2011.

[2]  Fakhry M. Khellah,et al.  Texture Classification Using Dominant Neighborhood Structure , 2011, IEEE Transactions on Image Processing.

[3]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[4]  Hanqing Lu,et al.  Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Huizhong Chen,et al.  Describing Clothing by Semantic Attributes , 2012, ECCV.

[6]  Luis E. Ortiz,et al.  Parsing clothing in fashion photographs , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Tamara L. Berg,et al.  Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items , 2013, 2013 IEEE International Conference on Computer Vision.

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  Ming Yang,et al.  Real-time clothing recognition in surveillance videos , 2011, 2011 18th IEEE International Conference on Image Processing.

[10]  Tong Zhang,et al.  Clothes search in consumer photos via color matching and attribute learning , 2011, ACM Multimedia.

[11]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[12]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.