Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem
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We consider a variational convex relaxation of a class of optimal partitioning and multiclass labeling problems, which has recently proven quite successful and can be seen as a continuous analogue of Linear Programming (LP) relaxation methods for finite-dimensional problems. While for the latter case several optimality bounds are known, to our knowledge no such bounds exist in the continuous setting. We provide such a bound by analyzing a probabilistic rounding method, showing that it is possible to obtain an integral solution of the original partitioning problem from a solution of the relaxed problem with an a priori upper bound on the objective, ensuring the quality of the result from the viewpoint of optimization. The approach has a natural interpretation as an approximate, multiclass variant of the celebrated coarea formula.
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