An approach to image segmentation based on shortest paths in graphs

Segmentation task plays an important role in image processing. In this paper, we attempt to extract information from images using texture analysis. Moreover, we propose characterization of pixels in images to define the similarity relation between them. These are based on textural information and findings of shortest paths in the graph representation of images. To reflect effectiveness of our method, we apply it to the benchmark Berkeley image database and we compare it to well-established image segmentation methods (sum and difference histograms for texture classification method, Mean-Shift method and mixture of Gaussian distributions method). The proposed approach achieves the best segmentation results measured by distance-based metrics. The experimental results show that our approach is efficient method for texture analysis and image segmentation.