Comparison of feature selection alogorithms for texture image classification

Vidya B. Manian; Armando Vega; Ramon E. Vasquez

doi:10.1117/12.438241

28 August 2001 Comparison of feature selection alogorithms for texture image classification

Vidya B. Manian, Armando Vega, Ramon E. Vasquez

Proceedings Volume 4388, Visual Information Processing X; (2001) https://doi.org/10.1117/12.438241
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Vidya B. Manian, Armando Vega, and Ramon E. Vasquez "Comparison of feature selection alogorithms for texture image classification", Proc. SPIE 4388, Visual Information Processing X, (28 August 2001); https://doi.org/10.1117/12.438241

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