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Locally connected graph embedding for semisupervised image classification

[+] Author Affiliations
Ke Lu

University of Electronic Science and Technology of China, School of Computer Science and Engineering, 610054, China

Zhengming Ding

University of Electronic Science and Technology of China, School of Computer Science and Engineering, 610054, China

Jidong Zhao

University of Electronic Science and Technology of China, School of Computer Science and Engineering, 610054, China

J. Electron. Imaging. 21(4), 043021 (Dec 13, 2012). doi:10.1117/1.JEI.21.4.043021
History: Received April 3, 2012; Revised October 25, 2012; Accepted November 14, 2012
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Abstract.  For classifying images with various appearances, graph embedding based subspace learning has difficulty in taking a comprehensive consideration of both local geometrical structure and between-class discriminative information. In addition, when no sufficient training samples exist, using only the simple weight graph corresponding to labeled samples, the embedding subspace may not be accurately modeled. We present a semisupervised graph embedding algorithm by combining graph embedding and sparse representation. This algorithm can effectively learn a compact and semantic subspace by using a locally connected graph, which can model the geometrical structure and essential correlation of subclusters within a class and can fully utilize both labeled and unlabeled samples. Moreover, using L2,1-norm, the proposed algorithm can preserve the sparse representation property of images from the original space in the lower dimensional projected space. Our experiments demonstrate that the proposed algorithm has better performance than the alternatives reported in recent literature.

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Citation

Ke Lu ; Zhengming Ding and Jidong Zhao
"Locally connected graph embedding for semisupervised image classification", J. Electron. Imaging. 21(4), 043021 (Dec 13, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.4.043021


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