Comparative study of image texture classification techniques

This paper contains study and review of various techniques used for feature extraction and texture classification. The objective of study is to find technique or combination of techniques to reduce complexity, speed while increasing the accuracy at the same time. Here we are studying and reviewing the three feature extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter method. Also classification methods like KNN, SVM, evolving fuzzy neural network (efunn), genetic algorithm and higher-order statistics. These are used on the texture datasets Brodatz, CUReT, VisTex and OuTex for the experimental purpose. In this paper, we present a comparative study of image texture classification techniques which are very much help full for image classifications