Comparison of Second Order Statistical Analysis and Wavelet Transform Method for Texture Image Classification
Ganesh S. Raghtate, Suresh Salankar · 2015
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.