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Feature subset selection for multi-class SVM based image classification

Journal Article


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Abstract


  • Multi-class image classification can benefit much from feature

    subset selection. This paper extends an error bound of binary SVMs

    to a feature subset selection criterion for the multi-class SVMs. By minimizing

    this criterion, the scale factors assigned to each feature in a

    kernel function are optimized to identify the important features. This

    minimization problem can be efficiently solved by gradient-based search

    techniques, even if hundreds of features are involved. Also, considering

    that image classification is often a small sample problem, the regularization

    issue is investigated for this criterion, showing its robustness in this

    situation. Experimental study on multiple benchmark image data sets

    demonstrates the effectiveness of the proposed approach.

Publication Date


  • 2007

Citation


  • Wang, L. (2007). Feature subset selection for multi-class SVM based image classification. Lecture Notes in Computer Science, 4844 145-154.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1654&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/648

Number Of Pages


  • 9

Start Page


  • 145

End Page


  • 154

Volume


  • 4844

Abstract


  • Multi-class image classification can benefit much from feature

    subset selection. This paper extends an error bound of binary SVMs

    to a feature subset selection criterion for the multi-class SVMs. By minimizing

    this criterion, the scale factors assigned to each feature in a

    kernel function are optimized to identify the important features. This

    minimization problem can be efficiently solved by gradient-based search

    techniques, even if hundreds of features are involved. Also, considering

    that image classification is often a small sample problem, the regularization

    issue is investigated for this criterion, showing its robustness in this

    situation. Experimental study on multiple benchmark image data sets

    demonstrates the effectiveness of the proposed approach.

Publication Date


  • 2007

Citation


  • Wang, L. (2007). Feature subset selection for multi-class SVM based image classification. Lecture Notes in Computer Science, 4844 145-154.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1654&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/648

Number Of Pages


  • 9

Start Page


  • 145

End Page


  • 154

Volume


  • 4844