Abstract:This work develops a global minimization framework for segmentation of high dimensional data into two classes. It combines recent convex optimization methods from imaging with recent graph based variational models for data segmentation. Two convex splitting algorithms are proposed, where graph-based PDE techniques are used to solve some of the subproblems. It is shown that global minimizers can be guaranteed for semisupervised segmentation with two regions. If constraints on the volume of the regions are incor… Show more
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