An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM

  • Björn Andres
  • Jörg H. Kappes
  • Ullrich Köthe
  • Christoph Schnörr
  • Fred A. Hamprecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)


Graphical models with higher order factors are an important tool for pattern recognition that has recently attracted considerable attention. Inference based on such models is challenging both from the view point of software design and optimization theory. In this article, we use the new C++ template library OpenGM to empirically compare inference algorithms on a set of synthetic and real-world graphical models with higher order factors that are used in computer vision. While inference algorithms have been studied intensively for graphical models with second order factors, an empirical comparison for higher order models has so far been missing. This article presents a first set of experiments that intends to fill this gap.


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Björn Andres
    • 1
  • Jörg H. Kappes
    • 1
  • Ullrich Köthe
    • 1
  • Christoph Schnörr
    • 1
  • Fred A. Hamprecht
    • 1
  1. 1.HCI, IWRUniversity of Heidelberg 

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