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Conference Paper

Multi-label cooperative cuts

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83994

Jegelka,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83814

Bilmes,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Jegelka, S., & Bilmes, J. (2011). Multi-label cooperative cuts. In CVPR 2011 Workshop on Inference in Graphical Models with Structured Potentials (pp. 1-4).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BB7A-B
Abstract
Recently, a family of global, non-submodular energy functions has been proposed that is expressed as coupling edges in a graph cut. This formulation provides a rich modelling framework and also leads to efficient approximate inference algorithms. So far, the results addressed binary random variables. Here, we extend these results to the multi-label case, and combine edge coupling with move-making algorithms.