Texture Classification Using Local Dissimilarity Maps of Gray-Level Co-Occurrence Matrices
Agnès Delahaies, Jérôme Landré, Frédéric Morain-Nicolier · 2024
In this paper, an original method for texture comparison and classification is presented. It is based on an adaptation of the Gray Local Dissimilarity Map (GLDM) for the comparison of textured images. In our method, GLDM of gray level co-occurrence matrices (GLCM) are computed instead of GLDM of images directly. Only one parameter is extracted from these GLDMs and used to classify textures. The method is tested on a texture dataset with two-class and multi-class classification using K-Nearest Neighbours (KNN) to prove its efficiency. The obtained results show that computing the GLDM of GLCM gives better performance for texture classification than both computing the GLDM of images and classification methods based on the extraction of several characteristics from GLCM.