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Proceedings Paper

Comparison of feature selection alogorithms for texture image classification
Author(s): Vidya B. Manian; Armando Vega; Ramon E. Vasquez
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Paper Abstract

Feature selection methods are useful to obtain an optimal set from a larger set thereby eliminating redundancy. In this paper, the popular methods of principal component analysis, Fisher discriminant analysis, and a genetic algorithm based approach are implemented for texture feature selection. The feature set is constituted by wavelet features. The selection processes are judged on using the classification rate of a particular classifier as a criterion. The Euclidean distance measure is used. The results show that the Fisher method performs better than principal component method. Also, the experiment concluded that the genetic algorithm improved the efficiency of all the methods except the Fisher method. Results of computation time are also presented.

Paper Details

Date Published: 28 August 2001
PDF: 9 pages
Proc. SPIE 4388, Visual Information Processing X, (28 August 2001); doi: 10.1117/12.438241
Show Author Affiliations
Vidya B. Manian, Univ. of Puerto Rico/Mayaguez (United States)
Armando Vega, Univ. of Puerto Rico/Mayaguez (United States)
Ramon E. Vasquez, Univ. of Puerto Rico/Mayaguez (United States)

Published in SPIE Proceedings Vol. 4388:
Visual Information Processing X
Stephen K. Park; Zia-ur Rahman; Robert A. Schowengerdt, Editor(s)

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