Texture Feature Extraction Algorithms

Texture feature extraction is a fundamental technique in image processing, particularly crucial in pattern recognition and computer vision applications. The Gray-Level Co-occurrence Matrix (GLCM) based method stands as one of the most classical approaches for texture feature description. This algorithm quantitatively characterizes texture attributes like coarseness and contrast by statistically analyzing the frequency of gray-level value combinations between pixel pairs at specific directional orientations and spatial distances within an image.

A standard implementation typically computes GLCMs along four principal directions (0°, 45°, 90°, 135°). Each matrix records the joint probability distribution of adjacent pixel gray-level values along its corresponding direction. For instance, the horizontal direction (0°) would count the frequency of gray-level combinations between each pixel and its immediate right neighbor. By comparing matrices across different orientations, the algorithm effectively captures directional heterogeneity in textures - such as the horizontal continuity in wood grain patterns or the diagonal regularity in woven fabrics.

The strength of this algorithm lies in its clear physical interpretation: high energy values in diagonal matrices may indicate斜向条纹 patterns, while uniformly distributed matrices with high entropy values suggest随机纹理. In practical implementations, statistical measures including contrast, correlation, energy, and homogeneity are commonly extracted from these matrices to form final feature vectors for classification or retrieval tasks. Programming implementations often utilize functions like graycomatrix() in MATLAB or OpenCV's texture analysis modules to efficiently compute these directional matrices and derive Haralick features.