Extraction Methods for Gray-Level Co-occurrence Matrix and Gray-Level Gradient Co-occurrence Matrix

Extracting image texture features is a critical step in image processing. Among various methods, Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Gradient Co-occurrence Matrix (GLGCM) stand as commonly used texture feature extraction approaches. GLCM characterizes image texture by quantifying spatial relationships between pixel intensity values, typically implemented in MATLAB using functions like graycomatrix() which calculates probability distributions of pixel pairs at specified offsets and distances. GLGCM extends this concept by incorporating gradient magnitude relationships between pixels, often involving preprocessing with edge detection operators (e.g., Sobel filters) before computing co-occurrence patterns. Both methods are natively supported in MATLAB's Image Processing Toolbox, enabling straightforward experimental research through functions that return statistical texture descriptors (contrast, correlation, energy, homogeneity) from computed matrices. Implementation typically involves parameter configuration for direction, distance, and quantization levels, followed by feature vector generation for pattern analysis applications.