Extracting Five Texture Features Using Gray Level Co-occurrence Matrix

The Gray Level Co-occurrence Matrix (GLCM) serves as a powerful method for extracting texture information from images. This statistical approach characterizes the spatial relationships between pixel intensity values by examining how frequently pairs of pixels with specific gray levels occur at a defined relative position. In implementation, the algorithm scans through the image comparing each pixel's gray value with its neighboring pixels based on specified offset parameters (typically distance and angle), then constructs a co-occurrence matrix that quantifies these spatial relationships. Through computational analysis of this matrix, we can derive texture features that enable effective image differentiation and pattern recognition.

From the GLCM, we can compute five fundamental texture features that provide distinct characteristics of image texture patterns. Entropy measures the randomness in pixel distribution - higher values indicate more complex and disordered textures, calculated using the probability distribution of gray level co-occurrences. Homogeneity (also called Inverse Difference Moment) quantifies how close the distribution of elements is to the GLCM diagonal - higher values suggest smoother and more uniform textures. Correlation describes the linear dependency of gray levels between neighboring pixels - higher values indicate stronger pixel relationships and more predictable patterns. Energy (also known as Angular Second Moment) represents the sum of squared elements in the GLCM - higher values correspond to more ordered and structured textures. Contrast measures the local intensity variations - higher values reveal more pronounced texture changes and edge information. These features are typically implemented using mathematical operations on the normalized GLCM, where each feature calculation involves specific matrix manipulations and probability computations.

Implementing GLCM-based texture analysis typically involves these key steps: image preprocessing and grayscale conversion, GLCM computation with defined parameters (distance, angle, and quantization levels), matrix normalization, and feature extraction using the mathematical formulas for each texture measure. This methodology provides valuable insights for image understanding and significantly enhances performance in computer vision applications such as image classification and material recognition.