Image Texture Feature Extraction Algorithm Based on Gray-Level Co-occurrence Matrix

This program implements an image texture feature extraction algorithm based on the Gray-Level Co-occurrence Matrix (GLCM). The algorithm computes GLCMs across four directional orientations (typically 0°, 45°, 90°, and 135°), extracts their corresponding texture feature vectors including contrast, correlation, energy, and homogeneity, and then calculates the mean values to improve computational stability. This approach substantially enhances processing efficiency while maintaining feature discrimination. To accelerate performance during matrix computations, parallel processing techniques can be implemented using MATLAB's parfor loops or GPU array operations. Additionally, alternative sophisticated texture extraction methods such as wavelet transform-based algorithms or deep learning approaches using convolutional neural networks (CNNs) could be integrated for richer and more accurate texture characterization. The extracted GLCM features can also be combined with other image descriptors like color histograms or shape features through feature concatenation or fusion techniques to create comprehensive image representations. Through continuous algorithmic improvements and expansions, the effectiveness and precision of image texture feature extraction can be progressively enhanced.