Clustering K-SVD for sparse representation of images

K-singular value decomposition (K-SVD) is a frequently used dictionary learning (DL) algorithm that iteratively works between sparse coding and dictionary updating. The sparse coding process generates sparse coefficients for each training sample, and the sparse coefficients induce clustering feature...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing Vol. 2019; no. 1; pp. 1 - 14
Main Authors: Fu, Jun, Yuan, Haikuo, Zhao, Rongqiang, Ren, Luquan
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 25.10.2019
Springer
Springer Nature B.V
SpringerOpen
Subjects:
Algorithms
Atomic properties
Clustering
Coding
Dictionaries
Dictionary learning
Engineering
Equipment and supplies
Greedy algorithms
Image processing
Machine learning
Quantum Information Technology
Representations
Signal,Image and Speech Processing
Singular value decomposition
Sparse representation
Spintronics
Image processing
Sparse representation
Dictionary learning
ISSN:1687-6180, 1687-6172, 1687-6180
Online Access:Get full text
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Summary:K-singular value decomposition (K-SVD) is a frequently used dictionary learning (DL) algorithm that iteratively works between sparse coding and dictionary updating. The sparse coding process generates sparse coefficients for each training sample, and the sparse coefficients induce clustering features. In the applications like image processing, the features of different clusters vary dramatically. However, all the atoms of dictionary jointly represent the features, regardless of clusters. This would reduce the accuracy of sparse representation. To address this problem, in this study, we develop the clustering K-SVD (CK-SVD) algorithm for DL and the corresponding greedy algorithm for sparse representation. The atoms are divided into a set of groups, and each group of atoms is employed to represent the image features of a specific cluster. Hence, the features of all clusters can be utilized and the number of redundant atoms are reduced. Additionally, two practical extensions of the CK-SVD are provided. Experimental results demonstrate that the proposed methods could provide more accurate sparse representation of images, compared to the conventional K-SVD and its existing extended methods. The proposed clustering DL model also has the potential to be applied to the online DL cases.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-019-0650-4