Adding GLCM texture analysis to a combined watershed transform and graph cut model for image segmentation

Abstract

Texture analysis is an important step in pattern recognition, image processing and computer vision systems. This work proposes an unsupervised approach to segment digital images combining the Watershed Transform and Normalized Cut in graphs (NCut) using texture information obtained from the Gray-Level Co-occurrence Matrix (GLCM). We corroborate the enhancement of image segmentation by means of the addition of texture analysis through several experiments carried out using the BSDS500 Berkeley dataset. For example, an improvement of 7% and 12% was found in relation to the Combined Watershed+NCut and Quadtree techniques, respectively. The overall performance of the proposed approach was indicated by the F-Measure through comparisons against other important segmentation methods.