  Texture classification is one of the problems which has been paid much
attention on by computer scientists since late 90s. If texture classification
is done correctly and accurately, it can be used in many cases such as Pattern
recognition, object tracking, and shape recognition. So far, there have been so
many methods offered to solve this problem. Near all these methods have tried
to extract and define features to separate different labels of textures really
well. This article has offered an approach which has an overall process on the
images of textures based on Local binary pattern and Gray Level Co-occurrence
matrix and then by edge detection, and finally, extracting the statistical
features from the images would classify them. Although, this approach is a
general one and is could be used in different applications, the method has been
tested on the stone texture and the results have been compared with some of the
previous approaches to prove the quality of proposed approach.
