Texture Feature Extraction Using Gray-Level Co-occurrence Matrix

There are various methods and techniques for texture feature extraction using Gray-Level Co-occurrence Matrix (GLCM). One prominent approach utilizes pixel-level gray value co-occurrence matrices to extract significant texture features for image classification and recognition. During this process, key feature parameters such as entropy (measuring texture randomness) and energy (representing textural uniformity) are computed to characterize different texture properties and attributes. In typical implementations, the GLCM is first constructed by analyzing pixel relationships at specified offsets and orientations, followed by calculating statistical measures using functions like graycoprops() in MATLAB or similar libraries. These extracted features can then be used to train machine learning models for automated classification and recognition of diverse textures within images. Consequently, GLCM-based texture feature extraction finds extensive applications in computer vision and image processing domains, particularly in medical imaging, remote sensing, and industrial inspection systems where texture analysis is crucial.