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  Global Connectivity Potentials for Random Field Models

Nowozin, S., & Lampert, C. (2009). Global Connectivity Potentials for Random Field Models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 818-825.

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Nowozin, S1, Autor           
Lampert, CH1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              

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 Zusammenfassung: Markov random field (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local interactions and cannot model global properties, such as connectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deriving a potential function that enforces the output labeling to be connected and that can naturally be used in the framework of recent MAP-MRF LP relaxations. Using techniques from polyhedral combinatorics, we show that a provably tight approximation to the MAP solution of the resulting MRF can still be found efficiently by solving a sequence of max-flow problems. The efficiency of the inference procedure also allows us to learn the parameters of a MRF with global connectivity potentials by means of a cutting plane algorithm. We experimentally evaluate our algorithm on both synthetic data and on the challenging segmentation task of the PASCAL VOC 2008 data set. We show that in both cases the addition of a connectedness prior significantly reduces the segmentation error.

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 Datum: 2009-06
 Publikationsstatus: Erschienen
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 Identifikatoren: URI: http://www.cvpr2009.org/
DOI: 10.1109/CVPRW.2009.5206567
BibTex Citekey: 5828
 Art des Abschluß: -

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Titel: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Veranstaltungsort: Miami Beach, FL, USA
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Titel: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Piscataway, NJ, USA : IEEE Service Center
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 818 - 825 Identifikator: -