A. Petrosino (Ed.): ICIAP 2013, Part II, LNCS 8157, pp. 510–521, 2013. © Springer-Verlag Berlin Heidelberg 2013 Texture Classification Based on Co-occurrence Matrix and Neuro-Morphological Approach Mohammed Talibi Alaoui 1 and Abderrahmane Sbihi 2 1 LAboratoire de Recherche en Informatique, LARI, Université Mohamed I, FSO, BP. 717, 60000, Oujda, Morocco m.talibialaoui@fso.ump.ma 2 Laboratoire LTI, ENSA, Université Abdelmalek Essaadi, Tanger, Morocco sbihi@ensat.ac.ma Abstract. This article proposes a hybrid approach for texture-based image clas- sification using the gray-level co-occurrence matrices (GLCM), self-organizing map (SOM) methods and mathematical morphology in an unsupervised context. The GLCM is a matrix of how often different combinations of pixel brightness values (grey levels) occur in an image. The GLCM matrices extracted from an image are processed to create the training data set for a SOM neural network. The SOM model organizes and extracts prototypes from various features ob- tained from the GLCM matrices. These prototypes are represented by the un- derlying probability density function (pdf). Under the assumption that each modal region of the underlying pdf corresponds to a one homogenous region in the texture image, the second part of the approach consists in partitioning the self-organizing map into connected modal regions by making concepts of mor- phological watershed transformation suitable for their detection. The classifica- tion process is then based on the so detected modal regions. We compare this approach to other texture feature extraction using fractal dimension. Keywords: Image Processing, Texture, Clustering, Co-occurrence Matrix, Self-Organizing Map, Watershed Transformation. 1 Introduction Texture is an important feature of objects in an image. The perception of texture is believed to play an important role in the human visual system for recognition and interpretation. There has been a great interest in the development of texture based pattern recognition methods in many different areas, especially in the areas of industrial automation, remote sensing and medical diagnosis [1]. Texture classification passes through the difficult step of texture representation or description. What is seen as a relatively easy task to the human observer becomes a difficult challenge when the analysis is made by a computational algorithm. How can we copy the human brain in its capability to analyze, classify and recognize textures?