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Image segmentation by adaptive nonconvex local and global subspace representation

[+] Author Affiliations
Cui-ling Wu, Wei-wei Wang

Xidian University, School of Mathematics and Statistics, 266 Xinglong Section of Xifeng Road, Xi’an 710126, China

J. Electron. Imaging. 25(3), 033026 (Jun 24, 2016). doi:10.1117/1.JEI.25.3.033026
History: Received February 25, 2016; Accepted June 7, 2016
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Abstract.  We formulate image segmentation as subspace clustering of image feature vectors. We propose a subspace representation model using a nonconvex extension of trace Lasso and a nonconvex approximation of rank function to regularize the subspace representation. The proposed model can adaptively capture the local and the global structures of the subspace representation so that the subspace representation can reveal the real subspace structure of the data and obtains excellent clustering performance. Experimental results show that the proposed model is better than the previous models in clustering and natural image segmentation.

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Cui-ling Wu and Wei-wei Wang
"Image segmentation by adaptive nonconvex local and global subspace representation", J. Electron. Imaging. 25(3), 033026 (Jun 24, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.3.033026


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