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キーワード:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
要旨:
Szeliski et al. published an influential study in 2006 on energy minimization
methods for Markov Random Fields (MRF). This study provided valuable insights
in choosing the best optimization technique for certain classes of problems.
While these insights remain generally useful today, the phenomenal success of
random field models means that the kinds of inference problems that have to be
solved changed significantly. Specifically, the models today often include
higher order interactions, flexible connectivity structures, large
la\-bel-spaces of different cardinalities, or learned energy tables. To reflect
these changes, we provide a modernized and enlarged study. We present an
empirical comparison of 32 state-of-the-art optimization techniques on a corpus
of 2,453 energy minimization instances from diverse applications in computer
vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2
framework and report extensive results regarding runtime and solution quality.
Key insights from our study agree with the results of Szeliski et al. for the
types of models they studied. However, on new and challenging types of models
our findings disagree and suggest that polyhedral methods and integer
programming solvers are competitive in terms of runtime and solution quality
over a large range of model types.
