Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition

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...

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Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 11; pp. 2651 - 2664
Main Authors:	Zhuolin Jiang, Zhe Lin, Davis, L. S.
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.11.2013 IEEE Computer Society
Subjects:	Algorithm design and analysis Algorithms Applied sciences Artificial intelligence Biometry - methods Classification algorithms Coding, codes Computer science; control theory; systems Dictionaries Discriminant Analysis Discriminative dictionary learning discriminative sparse-code error Exact sciences and technology Face - anatomy & histology Humans Image Interpretation, Computer-Assisted - methods Image reconstruction incremental dictionary learning Information, signal and communications theory label consistent K-SVD Linear programming Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Signal and communications theory supervised learning Support Vector Machine Telecommunications and information theory Testing Training Computer vision Discriminant analysis Dictionaries Form defect Pattern recognition Object recognition incremental dictionary learning label consistent K-SVD Experimental result Image matching Supervised learning Scene analysis Optimal solution Sparse representation Discriminative dictionary learning Objective function discriminative sparse-code error Learning algorithm Artificial intelligence Multiple classification Singular value decomposition
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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Abstract	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.
AbstractList	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. 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.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.
Author	Davis, L. S. Zhuolin Jiang Zhe Lin
Author_xml	– sequence: 1 surname: Zhuolin Jiang fullname: Zhuolin Jiang email: zhuolin@umiacs.umd.edu organization: Inst. for Adv. Comput. Studies, Univ. of Maryland, College Park, MD, USA – sequence: 2 surname: Zhe Lin fullname: Zhe Lin email: zlin@adobe.com organization: Adv. Technol. Labs., Adobe, San Jose, CA, USA – sequence: 3 givenname: L. S. surname: Davis fullname: Davis, L. S. email: lsd@umiacs.umd.edu organization: Inst. for Adv. Comput. Studies, Univ. of Maryland, College Park, MD, USA
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Keywords	Computer vision Discriminant analysis Dictionaries Form defect Pattern recognition Object recognition incremental dictionary learning label consistent K-SVD Experimental result Image matching Supervised learning Scene analysis Optimal solution Sparse representation Discriminative dictionary learning Objective function discriminative sparse-code error Learning algorithm Artificial intelligence Multiple classification Singular value decomposition
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Snippet	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...
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SubjectTerms	Algorithm design and analysis Algorithms Applied sciences Artificial intelligence Biometry - methods Classification algorithms Coding, codes Computer science; control theory; systems Dictionaries Discriminant Analysis Discriminative dictionary learning discriminative sparse-code error Exact sciences and technology Face - anatomy & histology Humans Image Interpretation, Computer-Assisted - methods Image reconstruction incremental dictionary learning Information, signal and communications theory label consistent K-SVD Linear programming Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Signal and communications theory supervised learning Support Vector Machine Telecommunications and information theory Testing Training
Title	Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition
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