A graph-based approach for contextual image segmentation

Image segmentation is one of the most important tasks in Image Analysis since it allows locating the relevant regions of the images and discarding irrelevant information. Any mistake during this phase may cause serious problems to the subsequent methods of the image-based systems. The segmentation process is usually very complex since most of the images present some kind of noise. In this work, two techniques are combined to deal with such problem: One derived from the graph theory and other from the anisotropic filtering methods, both emphasizing the use of contextual information in order to classify each pixel in the image with higher precision. Given a noisy grayscale image, an anisotropic diffusion filter is applied in order to smooth the interior regions of the image, eliminating noise without loosing much information of boundary areas. After that, a graph is built based on the pixels of the obtained diffused image, linking adjacent nodes (pixels) and considering the capacity of the edges as a function of the filter properties. Then, after applying the Ford-Fulkerson algorithm, the minimum cut of the graph is found (following the min cut-max flow theorem), segmenting the object of interest. The results show that the proposed approach outperforms the traditional and well-referenced Otsu's method.