PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-label cooperative cuts
Stefanie Jegelka and Jeff Bilmes
In: CVPR 2011 Workshop on Inference in Graphical Models with Structured Potentials, 20 June 2011, Colorado Springs, USA.

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.

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EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8777
Deposited By:Stefanie Jegelka
Deposited On:21 February 2012