Journal
PATTERN RECOGNITION LETTERS
Volume 130, Issue -, Pages 99-106Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2018.09.008
Keywords
Image classification; Sparse representation; Dictionary learning; Discrimination performance
Categories
Funding
- National Natural Science Foundation of China [61873155, 61672333, 61703096, 41471280]
- Natural Science Foundation of Shaanxi Province of China [2018JM6050]
- Ministry of Education Cooperation in Production and Education [201701023062]
- Key Science and Technology Program of Shaanxi Province [2016GY-081]
- Fundamental Research Funds for the Central Universities [GK201803088, GK201803059]
- China Postdoctoral Science Foundation [2017M611655]
- Program of Key Science and Technology Innovation Team in Shaanxi Province [2014KTC18]
- National Natural Science Foundation of Jiangsu Province [BK20170691]
- Interdisciplinary Incubation Project of Learning Science of Shaanxi Normal University
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Dictionary learning (DL) is a popular approach of image classification. Most DL methods ignore the information hidden in training samples or atoms, and thus cannot enhance the discrimination performance of a dictionary learning algorithm effectively. In addition, the training samples are prone to a wide range of variances such as sample noise and illumination change, which results in the degraded classification performance. Hence, in this paper, we propose a weighted constraint based dictionary learning algorithm to improve the classification performance of dictionary learning. More specifically, the proposed algorithm uses a diagonal weighted matrix to construct a constraint item for reducing the auto-correlation between atoms. Meanwhile, the training samples of the same class enjoy similar coding coefficients such that the reconfiguration and discrimination performance of dictionary is enhanced. Furthermore, in order to avoid over-fitting, we convert a strict two valued label matrix into a flexible matrix in the classification procedure allowing more degrees of freedom to fit the class labels. Experimental results show that the proposed algorithm outperforms massive state-of-the-art dictionary learning and sparse representation algorithms in image classification. (C) 2018 Elsevier B.V. All rights reserved.
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