Optimizing binary MRFs via extended roof duality

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Optimizing binary MRFs via extended roof duality (2007)

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by Carsten Rother , Vladimir Kolmogorov , Victor Lempitsky , Martin Szummer

Venue:	In Proc. CVPR
Citations:	171 - 12 self

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@TECHREPORT{Rother07optimizingbinary,
    author = {Carsten Rother and Vladimir Kolmogorov and Victor Lempitsky and Martin Szummer},
    title = {Optimizing binary MRFs via extended roof duality},
    institution = {In Proc. CVPR},
    year = {2007}
}

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Abstract

Many computer vision applications rely on the efficient optimization of challenging, so-called non-submodular, binary pairwise MRFs. A promising graph cut based approach for optimizing such MRFs known as “roof duality” was recently introduced into computer vision. We study two methods which extend this approach. First, we discuss an efficient implementation of the “probing ” technique introduced recently by Boros et al. [5]. It simplifies the MRF while preserving the global optimum. Our code is 400-700 faster on some graphs than the implementation of [5]. Second, we present a new technique which takes an arbitrary input labeling and tries to improve its energy. We give theoretical characterizations of local minima of this procedure. We applied both techniques to many applications, including image segmentation, new view synthesis, superresolution, diagram recognition, parameter learning, texture restoration, and image deconvolution. For several applications we see that we are able to find the global minimum very efficiently, and considerably outperform the original roof duality approach. In comparison to existing techniques, such as graph cut, TRW, BP, ICM, and simulated annealing, we nearly always find a lower energy. 1.

Keyphrases

roof duality binary mrfs graph cut efficient implementation probing technique image segmentation many application new view synthesis texture restoration several application theoretical characterization original roof duality approach image deconvolution local minimum computer vision arbitrary input efficient optimization new technique parameter learning diagram recognition global optimum many computer vision application binary pairwise mrfs

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