Multiclass Segmentation by Iterated ROF

Abstract. Variational models as the Mumford-Shah model and the ac-tive contour model have many applications in image segmentation. In this paper, we propose a new multiclass segmentation model by combining the Rudin-Osher-Fatemi model with an iterative thresholding procedure. We show that our new model for two classes is indeed equivalent to the Chan-Vese model but with an adapted regularization parameter which allows to segment classes with similar gray values. We propose an ef-ficient algorithm and discuss its convergence under certain conditions. Experiments on cartoon, texture and medical images demonstrate that our algorithm is not only fast but provides very good segmentation re-sults in comparison with other state-of-the-art segmentation models in particular for images containing classes of similar gray values.