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A Convex Discrete-Continuous Approach for Markov Random Fields

  • Christopher Zach
  • Pushmeet Kohli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

We propose an extension of the well-known LP relaxation for Markov random fields to explicitly allow continuous label spaces. Unlike conventional continuous formulations of labelling problems which assume that the unary and pairwise potentials are convex, our formulation allows them to be general piecewise convex functions with continuous domains. Furthermore, we present the extension of the widely used efficient scheme for handling L 1 smoothness priors over discrete ordered label sets to continuous label spaces. We provide a theoretical analysis of the proposed model, and empirically demonstrate that labelling problems with huge or continuous label spaces can benefit from our discrete-continuous representation.

Keywords

Unary Potential Markov Random Label Problem Match Cost Pairwise Potential 
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

  • Christopher Zach
    • 1
  • Pushmeet Kohli
    • 1
  1. 1.Microsoft Research CambridgeUK

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