A Hybrid Framework for Image Segmentation Using Probabilistic Integration of Heterogeneous Constraints

In this paper we present a new framework for image segmentation using probabilistic multinets. We apply this framework to integration of region-based and contour-based segmentation constraints. A graphical model is constructed to represent the relationship of the observed image pixels, the region labels and the underlying object contour. We then formulate the problem of image segmentation as the one of joint region-contour inference and learning in the graphical model. The joint inference problem is solved approximately in a band area around the estimated contour. Parameters of the model are learned on-line. The fully probabilistic nature of the model allows us to study the utility of different inference methods and schedules. Experimental results show that our new hybrid method outperforms methods that use homogeneous constraints.

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