Multi-label cooperative cuts

Jegelka, S.; Bilmes, J.

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Multi-label cooperative cuts

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Jegelka, S.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Bilmes, J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

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: https://hdl.handle.net/11858/00-001M-0000-0010-75BF-9

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.