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Sparse Representations of Image via Overcomplete Dictionary Learned by Adaptive Non-orthogonal Sparsifying Transform

Tang, Z., Yang, Z. and Ding, S. 2011, Sparse Representations of Image via Overcomplete Dictionary Learned by Adaptive Non-orthogonal Sparsifying Transform, in ICINIS 2010 : Proceedings of the 3rd International Conference on Intelligent Networks and Intelligent Systems 2010, IEEE Xplore, Piscataway, New Jersey, pp. 120-123, doi: 10.1109/ICINIS.2010.151.

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Title Sparse Representations of Image via Overcomplete Dictionary Learned by Adaptive Non-orthogonal Sparsifying Transform
Author(s) Tang, Z.
Yang, Z.
Ding, S.
Conference name International Conference on Intelligent Networks and Intelligent Systems (3rd : 2010 : Shenyang, China)
Conference location Sheyang, China
Conference dates 1-3 Nov. 2010
Title of proceedings ICINIS 2010 : Proceedings of the 3rd International Conference on Intelligent Networks and Intelligent Systems 2010
Editor(s) [Unknown]
Publication date 2011
Conference series Intelligent Networks and Intelligent Systems International Conference
Start page 120
End page 123
Total pages 4
Publisher IEEE Xplore
Place of publication Piscataway, New Jersey
Keyword(s) K-SVD
non-orthogonal sparsifying transform
overcomplete dictionary
sparse representations
Summary How to learn an over complete dictionary for sparse representations of image is an important topic in machine learning, sparse coding, blind source separation, etc. The so-called K-singular value decomposition (K-SVD) method [3] is powerful for this purpose, however, it is too time-consuming to apply. Recently, an adaptive orthogonal sparsifying transform (AOST) method has been developed to learn the dictionary that is faster. However, the corresponding coefficient matrix may not be as sparse as that of K-SVD. For solving this problem, in this paper, a non-orthogonal iterative match method is proposed to learn the dictionary. By using the approach of sequentially extracting columns of the stacked image blocks, the non-orthogonal atoms of the dictionary are learned adaptively, and the resultant coefficient matrix is sparser. Experiment results show that the proposed method can yield effective dictionaries and the resulting image representation is sparser than AOST.
ISBN 9781424485482
1424485487
Language eng
DOI 10.1109/ICINIS.2010.151
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30059210

Document type: Conference Paper
Collection: School of Information Technology
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