Volume 46, pp. 36-54, 2017.

Sparsity-inducing variational shape partitioning

Serena Morigi and Martin Huska


We propose a sparsity-inducing multi-channel multiple region model for the efficient partitioning of a mesh into salient parts. Our approach is based on rewriting the Mumford-Shah models in terms of piece-wise smooth/constant functionals that incorporate a non-convex regularizer for minimizing the boundary lengths. The solution of this optimization problem, obtained by an efficient proximal forward backward algorithm, is used by a simple thresholding/clusterization procedure to segment the shape into the required number of parts. Therefore, it is not necessary to further solve the optimization problem for a different number of partitioning regions. Experimental results show the effectiveness and efficiency of our proposals when applied to both single- and multi-channel (shape characterizing) functions.

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Key words

mesh decomposition, variational segmentation, non-convex minimization, spectral clustering

AMS subject classifications

65M10, 78A48.

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