Probabilistic image segmentation with closedness constraints

Authors:
Bjoern Andres;Jorg H. Kappes;Thorsten Beier;Ullrich Kothe;Fred A. Hamprecht
Affiliations:
HCI, University of Heidelberg, Speyerer Str. 6, 69115, Germany;HCI, University of Heidelberg, Speyerer Str. 6, 69115, Germany;HCI, University of Heidelberg, Speyerer Str. 6, 69115, Germany;HCI, University of Heidelberg, Speyerer Str. 6, 69115, Germany;HCI, University of Heidelberg, Speyerer Str. 6, 69115, Germany
Venue:
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Year:
2011

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Abstract

We propose a novel graphical model for probabilistic image segmentation that contributes both to aspects of perceptual grouping in connection with image segmentation, and to globally optimal inference with higher-order graphical models. We represent image partitions in terms of cellular complexes in order to make the duality between connected regions and their contours explicit. This allows us to formulate a graphical model with higher-order factors that represent the requirement that all contours must be closed. The model induces a probability measure on the space of all partitions, concentrated on perceptually meaningful segmentations. We give a complete polyhedral characterization of the resulting global inference problem in terms of the multicut polytope and efficiently compute global optima by a cutting plane method. Competitive results for the Berkeley segmentation benchmark confirm the consistency of our approach.