Multiscale Graph Theory Based Color Segmentation

In this paper, image segmentation is addressed within the framework of nonlinear multiscale watersheds in combination with graph theory. First, a graph is created which decomposes the image in scale and space using the concept of multiscale watersheds. In the subsequent step the obtained graph is partitioned using recursive graph cuts in a coarse to fine manner. In this way, we combine scale and feature measures in a flexible way. The dissimilarity between graph-nodes is estimated by using the Earth mover´s distance on a featureset that combines color, scale and contrast. Experimental results demonstrate the efficiency of the proposed method for natural scene images.