Comparison of feature selection alogorithms for texture image classification
Vidya B. Manian, Armando Vega, Ramon E. Vasquez · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
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.