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Fusion Moves for Markov Random Field Optimization

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by Victor Lempitsky , Carsten Rother , Stefan Roth , Andrew Blake
Citations:68 - 5 self
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BibTeX

@MISC{Lempitsky_fusionmoves,
    author = {Victor Lempitsky and Carsten Rother and Stefan Roth and Andrew Blake},
    title = { Fusion Moves for Markov Random Field Optimization},
    year = {}
}

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Abstract

The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one possible way of achieving this by using graph cuts to combine pairs of suboptimal labelings or solutions. We call this combination process the fusion move. By employing recently developed graph cut based algorithms (so-called QPBO-graph cut), the fusion move can efficiently combine two proposal labelings in a theoretically sound way, which is in practice often globally optimal. We demonstrate that fusion moves generalize many previous graph cut approaches, which allows them to be used as building block within a broader variety of optimization schemes than were considered before. In particular, we propose new optimization schemes for computer vision MRFs with applications to image restoration, stereo, and optical flow, among others. Within these schemes the fusion moves are used 1) for the parallelization of MRF optimization into several threads; 2) for fast MRF optimization by combining cheapto-compute solutions; and 3) for the optimization of highly non-convex continuous-labeled MRFs with 2D labels. Our final example is a non-vision MRF concerned with cartographic label placement, where fusion moves can be used to improve the performance of a standard inference method (loopy belief propagation).

Keyphrases

fusion move    graph cut    markov random field optimization    markov random field    continuous label    building block    multiple discrete    computer vision mrfs    open question    loopy belief propagation    suboptimal labelings    combination process    so-called qpbo-graph cut    non-vision mrf    possible way    final example    mrf optimization    non-convex continuous-labeled mrfs    many previous graph cut approach    optimization scheme    several thread    standard inference method    new optimization scheme    fast mrf optimization    cartographic label placement    proposal labelings    efficient application    optical flow    cheapto-compute solution    sound way    image restoration   

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