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Title: Minimizing Count-based High Order Terms in Markov Random Fields
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Authors: Schoenemann, Thomas
Year: 2011
Document Type:Conference Paper
Conference: EMMCVPR
Conference location: St Petersburg
Status: Published
Refereed: Yes
BibTeX item:BibTeX
Abstract: We present a technique to handle computer vision problems inducing models with very high order terms - in fact terms of maximal order. Here we consider terms where the cost function depends only on the number of variables that are assigned a certain label, but where the dependence is arbitrary. Applications include image segmentation with a histogram-based data term [28] and the recently introduced marginal probability elds [31]. The presented technique makes use of linear and integer linear program- ming.We include a set of customized cuts to strengthen the formulations.




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