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Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition

[+] Author Affiliations
Xiaoning Song

Jiangnan University, Department of computer science, School of Internet of Things Engineering, Wuxi 214122, China

University of Surrey, Centre for Vision, Speech and Signal Processing, Department of Electronic Engineering, Guildford GU2 7XH, United Kingdom

Zhen-Hua Feng, Guosheng Hu

University of Surrey, Centre for Vision, Speech and Signal Processing, Department of Electronic Engineering, Guildford GU2 7XH, United Kingdom

Xibei Yang, Yunsong Qi

Jiangsu University of Science and Technology, Department of computer science, School of Computer Science and Engineering, Zhenjiang 212003, China

Jingyu Yang

Nanjing University of Science and Technology, Department of computer science, School of Computer Science and Engineering, Zhenjiang 212003, China

J. Electron. Imaging. 24(5), 053010 (Sep 21, 2015). doi:10.1117/1.JEI.24.5.053010
History: Received April 11, 2015; Accepted August 18, 2015
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Abstract.  This paper proposes a progressive sparse representation-based classification algorithm using local discrete cosine transform (DCT) evaluation to perform face recognition. Specifically, the sum of the contributions of all training samples of each subject is first taken as the contribution of this subject, then the redundant subject with the smallest contribution to the test sample is iteratively eliminated. Second, the progressive method aims at representing the test sample as a linear combination of all the remaining training samples, by which the representation capability of each training sample is exploited to determine the optimal “nearest neighbors” for the test sample. Third, the transformed DCT evaluation is constructed to measure the similarity between the test sample and each local training sample using cosine distance metrics in the DCT domain. The final goal of the proposed method is to determine an optimal weighted sum of nearest neighbors that are obtained under the local correlative degree evaluation, which is approximately equal to the test sample, and we can use this weighted linear combination to perform robust classification. Experimental results conducted on the ORL database of faces (created by the Olivetti Research Laboratory in Cambridge), the FERET face database (managed by the Defense Advanced Research Projects Agency and the National Institute of Standards and Technology), AR face database (created by Aleix Martinez and Robert Benavente in the Computer Vision Center at U.A.B), and USPS handwritten digit database (gathered at the Center of Excellence in Document Analysis and Recognition at SUNY Buffalo) demonstrate the effectiveness of the proposed method.

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Citation

Xiaoning Song ; Zhen-Hua Feng ; Guosheng Hu ; Xibei Yang ; Jingyu Yang, et al.
"Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition", J. Electron. Imaging. 24(5), 053010 (Sep 21, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.5.053010


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