Texture Classification Based on Co-occurrence Matrix and Neuro-Morphological Approach

This article proposes a hybrid approach for texture-based image classification 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 obtained from the GLCM matrices. These prototypes are represented by the underlying 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 morphological watershed transformation suitable for their detection. The classification process is then based on the so detected modal regions. We compare this approach to other texture feature extraction using fractal dimension.