Texture Classification Approach Based on Combination of Edge & Co-occurrence and Local Binary Pattern

TL;DR

This paper proposes a texture classification method combining Local Binary Pattern, Gray Level Co-occurrence matrix, and edge detection, demonstrating improved accuracy on stone textures compared to previous approaches.

Contribution

It introduces a novel integrated approach that combines multiple feature extraction techniques for more effective texture classification.

Findings

Achieved higher classification accuracy on stone textures

Demonstrated the effectiveness of combining edge detection with statistical features

Compared favorably with existing texture classification methods

Abstract

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.