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Nonmetric Priors for

2020-04-02 | |  91 |   41 |   0

Abstract

We propose a novel convex prior for multilabel optimization which allows to impose arbitrary distances between labels. Only sym- metry, d(i, j ) ? 0 and d(i, i) = 0 are required. In contrast to previous grid based approaches for the nonmetric case, the proposed prior is for- mulated in the continuous setting avoiding grid artifacts. In particular, the model is easy to implement, provides a convex relaxation for the Mumford-Shah functional and yields comparable or superior results on the MSRC segmentation database comparing to metric or grid based approaches.

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