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Journal of Electronic Imaging

Image classification by semisupervised sparse coding with confident unlabeled samples
Author(s): Xiao Li; Min Fang; Jinqiao Wu; Liang He; Xian Tian
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Paper Abstract

Sparse coding has achieved very excellent performance in image classification tasks, especially when the supervision information is incorporated into the dictionary learning process. However, there is a large amount of unlabeled samples that are expensive and boring to annotate. We propose an image classification algorithm by semisupervised sparse coding with confident unlabeled samples. In order to make the learnt sparse coding more discriminative, we select and annotate some confident unlabeled samples. A minimization model is developed in which the reconstruction error of the labeled, the selected unlabeled and the remaining unlabeled data and the classification error are integrated, which enhances the discriminant property of the dictionary and sparse representations. The experimental results on image classification tasks demonstrate that our algorithm can significantly improve the image classification performance.

Paper Details

Date Published: 20 September 2017
PDF: 9 pages
J. Electron. Imag. 26(5) 053013 doi: 10.1117/1.JEI.26.5.053013
Published in: Journal of Electronic Imaging Volume 26, Issue 5
Show Author Affiliations
Xiao Li, Xidian Univ. (China)
Min Fang, Xidian Univ. (China)
Jinqiao Wu, Xidian Univ. (China)
Liang He, Xidian Univ. (China)
Xian Tian, Xidian Univ. (China)


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