Comparison of Second Order Statistical Analysis and Wavelet Transform Method for Texture Image Classification

Abstract

The quality of texture image classification depends on quality of texture features and classification algorithms. Most important is to select texture features with highly discriminative to inter-class textures. In this paper, features are extracted from texture images using Gray Level Co -- occurrence method and Wavelet method. Haralick features with a feed forward neural network show classification accuracy of 98.21%, while Wavelet features show classification accuracy of 96.05% for the same data. These results show that Haralick features are suitable for texture classification.