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Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition
Nov. 2013 (vol. 35 no. 11)
pp. 2651-2664
Zhuolin Jiang, Inst. for Adv. Comput. Studies, Univ. of Maryland, College Park, MD, USA
Zhe Lin, Adv. Technol. Labs., Adobe, San Jose, CA, USA
L. S. Davis, Inst. for Adv. Comput. Studies, Univ. of Maryland, College Park, MD, USA
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 consistency 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 overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. 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, action, scene, and object category recognition under the same learning conditions.
Index Terms:
Dictionaries,Linear programming,Classification algorithms,Training,Algorithm design and analysis,Image reconstruction,Testing,discriminative sparse-code error,Discriminative dictionary learning,incremental dictionary learning,supervised learning,label consistent K-SVD
Zhuolin Jiang, Zhe Lin, L. S. Davis, "Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2651-2664, Nov. 2013, doi:10.1109/TPAMI.2013.88
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