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dc.contributor.authorBae, Egileng
dc.contributor.authorYuan, Jingeng
dc.contributor.authorTai, Xue-Chengeng
dc.PublishedInternational Journal of Computer Vision 92(1): 112-129en
dc.description.abstractThis paper is devoted to the optimization problem of continuous multi-partitioning, or multi-labeling, which is based on a convex relaxation of the continuous Potts model. In contrast to previous efforts, which are tackling the optimal labeling problem in a direct manner, we first propose a novel dual model and then build up a corresponding dualitybased approach. By analyzing the dual formulation, sufficient conditions are derived which show that the relaxation is often exact, i.e. there exists optimal solutions that are also globally optimal to the original nonconvex Potts model. In order to deal with the nonsmooth dual problem, we develop a smoothing method based on the log-sum exponential function and indicate that such a smoothing approach leads to a novel smoothed primal-dual model and suggests labelings with maximum entropy. Such a smoothing method for the dual model also yields a new thresholding scheme to obtain approximate solutions. An expectation maximization like algorithm is proposed based on the smoothed formulation which is shown to be superior in efficiency compared to earlier approaches from continuous optimization. Numerical experiments also show that our method outperforms several competitive approaches in various aspects, such as lower energies and better visual quality.en_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartof<a href="" target="blank">Efficient global minimization methods for variational problems in imaging and vision</a>en_US
dc.rightsAttribution-NonCommercial CC BY-NCeng
dc.subjectConvex relaxationeng
dc.subjectImage segmentationeng
dc.subjectPrimal-dual methodseng
dc.subjectTotal variationeng
dc.titleGlobal Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approachen_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright The Author(s). This article is published with open access at Licence: Attribution-NonCommercial 2.5 Generic (CC BY-NC 2.5) .en_US
dc.subject.nsiVDP::Mathematics and natural science: 400::Mathematics: 410en_US
dc.subject.nsiVDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation, visualization, signal processing, image processing: 429en_US

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