Extracting Image Texture Features Using Gray-Level Co-occurrence Matrix

In this document, we explore the methodology for extracting image texture features through gray-level co-occurrence matrix (GLCM) analysis. The implementation is carried out using MATLAB, where we demonstrate the complete workflow including feature extraction and subsequent classification using fuzzy c-means clustering algorithm. The GLCM implementation involves calculating spatial relationships between pixel pairs at specific distances and orientations, typically using functions like graycomatrix() to generate the co-occurrence matrix and graycoprops() to derive statistical features such as contrast, correlation, energy, and homogeneity. The classification phase employs fuzzy c-means clustering through the fcm() function, which handles the uncertainty in texture pattern assignments by allowing partial membership in multiple clusters. This integrated approach enables comprehensive understanding of image texture characteristics and achieves more accurate image classification results by combining statistical texture descriptors with soft computing techniques.