Gabor Filter Parameters Optimization for Texture Classification Based on Genetic Algorithm

Abstract

Despite Gabor filtering has emerged as one of the leading techniques for texture classification, a unifying approach to its adoption has not emerged yet. As it is true for Gabor filter bank, the design of a filter bank consists of the selection of a proper set of values for the filter parameters. In this paper, it is intended to find a set of Gabor filter bank parameters optimized for the performance of texture classification system. The application method is suggested to compute Gabor filter parameters based on Genetic Algorithm (GA). The parameters are optimized according to each group of textures. We tested the proposed method with several texture images using a standard database. The experimental results demonstrate the effectiveness of proposed approach as the overall success is about 97.5%.