Histogram Based Segmentation Using Wasserstein Distances

13 years 10 months ago

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In this paper, we propose a new nonparametric region-based active contour model for clutter image segmentation. To quantify the similarity between two clutter regions, we propose to compare their respective histograms using the Wasserstein distance. Our ﬁrst segmentation model is based on minimizing the Wasserstein distance between the object (resp. background) histogram and the object (resp. background) reference histogram, together with a geometric regularization term that penalizes complicated region boundaries. The minimization is achieved by computing the gradient of the level set formulation for the energy. Our second model does not require reference histograms and assumes that the image can be partitioned into two regions in each of which the local histograms are similar everywhere. Key words: image segmentation, region-based active contour, Wasserstein distance, clutter

Tony F. Chan, Selim Esedoglu, Kangyu Ni

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