Spatially Varying Color Distributions for Interactive Multilabel Segmentation

We propose a method for interactive multilabel segmentation which explicitly takes into account the spatial variation of color distributions. To this end, we estimate a joint distribution over color and spatial location using a generalized Parzen density estimator applied to each user scribble. In this way, we obtain a likelihood for observing certain color values at a spatial coordinate. This likelihood is then incorporated in a Bayesian MAP estimation approach to multiregion segmentation which in turn is optimized using recently developed convex relaxation techniques. These guarantee global optimality for the two-region case (foreground/background) and solutions of bounded optimality for the multiregion case. We show results on the GrabCut benchmark, the recently published Graz benchmark, and on the Berkeley segmentation database which exceed previous approaches such as GrabCut [32], the Random Walker [15], Santner's approach [35], TV-Seg [39], and interactive graph cuts [4] in accuracy. Our results demonstrate that taking into account the spatial variation of color models leads to drastic improvements for interactive image segmentation.