Learning Structured Low-Rank Representations for Image Classification

Yangmuzi Zhang, Zhuolin Jiang, Larry S. Davis; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 676-683

Abstract


An approach to learn a structured low-rank representation for image classification is presented. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier. Experimental results demonstrate the effectiveness of our approach.

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[bibtex]
@InProceedings{Zhang_2013_CVPR,
author = {Zhang, Yangmuzi and Jiang, Zhuolin and Davis, Larry S.},
title = {Learning Structured Low-Rank Representations for Image Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}