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ARTICLE

Weighted Sparse Image Classification Based on Low Rank Representation

Qidi Wu1, Yibing Li1, Yun Lin1,*, Ruolin Zhou2
College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China.
Department of Electrical and Computer Engineering, Western New England University,USA.
* Corresponding Author: Yun Lin. Email: .

Computers, Materials & Continua 2018, 56(1), 91-105. https://doi.org/ 10.3970/cmc.2018.02771

Abstract

The conventional sparse representation-based image classification usually codes the samples independently, which will ignore the correlation information existed in the data. Hence, if we can explore the correlation information hidden in the data, the classification result will be improved significantly. To this end, in this paper, a novel weighted supervised spare coding method is proposed to address the image classification problem. The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation. And then, it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in a supervised way. Experimental results show that the proposed method is superiority to many conventional image classification methods.

Keywords

Image classification, sparse representation, low-rank representation, numerical optimization.

Cite This Article

Q. . Wu, Y. . Li, Y. . Lin and R. . Zhou, "Weighted sparse image classification based on low rank representation," Computers, Materials & Continua, vol. 56, no.1, pp. 91–105, 2018.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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