%0 Conference Paper
%B Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
%D 2011
%T Learning a discriminative dictionary for sparse coding via label consistent K-SVD
%A Zhuolin Jiang
%A Zhe Lin
%A Davis, Larry S.
%K classification error
%K Dictionaries
%K dictionary learning process
%K discriminative sparse code error
%K face recognition
%K image classification
%K Image coding
%K K-SVD
%K label consistent
%K learning (artificial intelligence)
%K object category recognition
%K Object recognition
%K optimal linear classifier
%K reconstruction error
%K singular value decomposition
%K Training data
%X A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistent constraint called `discriminative sparse-code error' and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single over-complete dictionary and an optimal linear classifier jointly. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse coding techniques for face and object category recognition under the same learning conditions.
%B Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
%P 1697 - 1704
%8 2011/06//
%G eng
%R 10.1109/CVPR.2011.5995354