Multiclass pixel labeling with non-local matching constraints

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Multiclass pixel labeling with non-local matching constraints
A popular approach to pixel labeling problems, such as multiclass image segmentation, is to construct a pairwise conditional Markov random field (CRF) over image pixels where the pairwise term encodes a preference for smoothness within local 4-connected or 8-connected pixel neighborhoods. Recently, researchers have considered higherorder models that encode soft non-local constraints (e.g., label consistency, connectedness, or co-occurrence statistics). These new models and the associated energy minimization algorithms have significantly pushed the state-ofthe-art for pixel labeling problems. In this paper, we consider a new non-local constraint that penalizes inconsistent pixel labels between disjoint image regions having similar appearance. We encode this constraint as a truncated higher-order matching potential function between pairs of image regions in a conditional Markov random field model and show how to perform efficient approximate MAP inference in the model. We experiment...
Stephen Gould
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Stephen Gould
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