NDLI: Self-optimized two phase test sample sparse representation method for image classification

Content Provider	IEEE Xplore Digital Library
Author	Dornaika, F. El Traboulsi, Y. Hernandez, C. Assoum, A.
Copyright Year	2013
Description	Author affiliation: Lebanese Univ., Tripoli, Lebanon (Assoum, A.) \|\| Univ. of the Basque Country UPV/EHU, San Sebastian, Spain (Dornaika, F.; El Traboulsi, Y.; Hernandez, C.)
Abstract	Sparse Representation Classifiers and their variants are more and more used by computer vision and signal processing communities due to their good performance. Recently, it has been shown that the performance of Sparse Representation Classifiers and their variants in terms of accuracy and computational complexity can be enhanced by simply including a two-phase coding scheme regardless of the used coding scheme. The two-phase strategies use different schemes for selecting the examples that should be handed over to the next coding phase. However, all of them use a fixed and predefined number for these examples making the performance of the final classifier very dependent on this choice. This paper introduces three strategies for self-optimized size selection associated with Two Phase Test Sample Sparse Representation method. Experiments conducted on three face data sets show that the introduced scheme can outperform the classic two-phase strategies. Although the experiments were conducted on face data sets, the proposed schemes can be useful for a broad spectrum of pattern recognition problems.
Starting Page	163
Ending Page	166
File Size	249464
Page Count	4
File Format	PDF
ISBN	9781479902491
e-ISBN	9781479902514
DOI	10.1109/ICABME.2013.6648873
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-11
Publisher Place	Lebanon
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Face recognition Collaborative Neighborhood Representation Encoding Face Equations Testing Image classification Sparse Representation Classifier
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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