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Statistical priors for combinatorial optimization: efficient solutions via graph cuts
Statistical priors for combinatorial optimization: efficient solutions via graph cuts
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机译：组合优化的统计先验：通过图割的有效解决方案
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摘要
Methods of statistical learning for Bayesian inference in the context of efficient optimization schemes for image restoration are presented. Second and third order priors that may be learned while maintaining graph representability are identified. A framework to learn and impose prior knowledge on the distribution of pairs and triplets of labels via graph cuts is presented. The disclosed methods optimally restore binary textures from very noisy images with runtimes in the order of seconds while imposing hundreds of statistically learned constraints per node.
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