Nonmetric Priors for Continuous Multilabel Optimization

  • Evgeny Strekalovskiy
  • Claudia Nieuwenhuis
  • Daniel Cremers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)


We propose a novel convex prior for multilabel optimization which allows to impose arbitrary distances between labels. Only symmetry, 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 formulated 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.


Distance Function Continuous Setting Convex Relaxation Block Artifact Label Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Evgeny Strekalovskiy
    • 1
  • Claudia Nieuwenhuis
    • 1
  • Daniel Cremers
    • 1
  1. 1.Technical University MunichGermany

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