Comparison of feature selection algorithms for texture image classification

摘要

Feature selection methods are useful to obtain an optimal set from a larger ser 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.