Dipartimento d'Ingegneria

Grey-level co-occurrence matrices (GLCM) have been on the scene for almost forty years and continue to be widely used today. We developed a method to improve accuracy and robustness against rotation of GLCM features for image classification. Co-occurrences are computed through digital circles as an alternative to the standard four directions. We use discrete Fourier transform normalization to convert rotation dependent features into rotation invariant ones. We tested or method on four different datasets of natural and synthetic images. Experimental results show that our approach is more accurate and robust against rotation than the standard GLCM features.