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- W1887340714 abstract "AbstractIn this chapter we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (ps) and Markov random fields (mrfs) both of which have efficient algorithms for their solution. The resulting combination, which we call the object category specific mrf, suggests a solution to the problem that has long dogged mrfs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the ps. We develop an efficient method, ObjCut, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the ps model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.KeywordsPairwise PotentialPictorial StructurePart LabelChamfer DistanceTree CascadeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1887340714 date "2006-01-01" @default.
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- W1887340714 title "An Object Category Specific mrf for Segmentation" @default.
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- W1887340714 doi "https://doi.org/10.1007/11957959_30" @default.
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