Locally connected graph embedding for semisupervised image classification

Ke Lu; Zhengming Ding; Jidong Zhao

doi:10.1117/1.JEI.21.4.043021

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.

Citation Download Citation

Ke Lu, Ke Lu, Zhengming Ding, Zhengming Ding, Jidong Zhao, Jidong Zhao, } "Locally connected graph embedding for semisupervised image classification," Journal of Electronic Imaging 21(4), 043021 (13 December 2012). https://doi.org/10.1117/1.JEI.21.4.043021 . Submission:

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