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Journal of Electronic Imaging

Locally connected graph embedding for semisupervised image classification
Author(s): Ke Lu; Zhengming Ding; Jidong Zhao
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Paper 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.

Paper Details

Date Published: 13 December 2012
PDF: 10 pages
J. Electron. Imaging. 21(4) 043021 doi: 10.1117/1.JEI.21.4.043021
Published in: Journal of Electronic Imaging Volume 21, Issue 4
Show Author Affiliations
Ke Lu, Univ. of Electronic Science and Technology of China (China)
Zhengming Ding, Univ. of Electronic Science and Technology of China (China)
Jidong Zhao, Univ. of Electronic Science and Technology of China (China)


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