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Multicuts and perturb & MAP for probabilistic graph clustering

We present a probabilistic graphical model formulation for the graph clustering problem. This enables us to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and t...

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Hauptverfasser: Kappes, Jörg Hendrik (VerfasserIn) , Swoboda, Paul (VerfasserIn) , Schnörr, Christoph (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: October 2016
In: Journal of mathematical imaging and vision
Year: 2016, Jahrgang: 56, Heft: 2, Pages: 221-237
ISSN:1573-7683
DOI:10.1007/s10851-016-0659-3
Online-Zugang:Resolving-System, Volltext: http://dx.doi.org/10.1007/s10851-016-0659-3
Verlag, Volltext: https://link.springer.com/article/10.1007/s10851-016-0659-3
Volltext
Verfasserangaben:Jörg Hendrik Kappes, Paul Swoboda, Bogdan Savchynskyy, Tamir Hazan, Christoph Schnörr

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520 |a We present a probabilistic graphical model formulation for the graph clustering problem. This enables us to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. We exploit recent progress on globally optimal MAP inference by integer programming and on perturbation-based approximations of the log-partition function, in order to sample clusterings and to estimate marginal distributions of node-pairs both more accurately and more efficiently than state-of-the-art methods. Our approach works for any graphically represented problem instance. This is demonstrated for image segmentation and social network cluster analysis. Our mathematical ansatz should be relevant also for other combinatorial problems. 
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