Texture Feature Extraction using Gray-Level Co-occurrence Matrix and Gray-Level Gradient Co-occurrence Matrix

Texture features constitute a fundamental component in image processing, serving critical roles in image classification and analysis. The Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Gradient Co-occurrence Matrix (GLGCM) represent widely adopted methodologies for extracting texture characteristics. These matrices computationally capture spatial relationships between pixel intensity values by analyzing the frequency of co-occurring gray levels at specified relative positions. Implementation typically involves defining displacement vectors (distance and orientation parameters) to generate statistical measures including contrast, correlation, energy, and homogeneity. The GLGCM further incorporates gradient magnitude information to enhance texture discrimination capabilities, making it particularly effective for analyzing complex surface patterns. These extracted texture descriptors enable robust identification of distinct objects and surface materials, finding extensive applications in computer vision systems, medical image analysis, remote sensing, and industrial inspection processes. Code implementation often utilizes matrix convolution operations and probability distribution calculations to derive rotation-invariant features.