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• W2090858602 abstract "Spatially constrained Dirichlet process mixture models are springing up in image processing in recent years. However, inference for the model is NP-hard. Gibbs sampling which is a generic Markov chain Monte Carlo technique is commonly employed for the model inference. It needs to traverse all the nodes of the constructed graph in each iteration. The sampling process hardly crosses over the intermediate low probabilistic state. In addition, it is not well informed by the spatial relationship in the sampling process. In this paper, a spatially dependent split-merge algorithm for sampling the MRF/DPMM model based on Swendsen-Wang Cuts is proposed. It is a state of the art algorithm which combines the spatial relationship to direct the sampling, and lessen the mixing time drastically. In this algorithm, a set of nodes are being frozen together according to the discriminative probability of the edges between neighboring nodes. The frozen nodes update their states simultaneously in contrast to the single node update in a Gibbs sampling. The final step of the algorithm is to accept the proposed new state according to the Metropolis Hasting scheme, in which only the ratio of posterior distribution needs to be calculated in each iteration. Experimental results demonstrated that the proposed sampling algorithm is able to reduce the mixing time considerably. At the same time, it can obtain comparably stable results with a random initial state." @default.
• W2090858602 created "2016-06-24" @default.
• W2090858602 creator A5066282713 @default.
• W2090858602 creator A5088023300 @default.
• W2090858602 date "2014-09-01" @default.
• W2090858602 modified "2023-09-27" @default.
• W2090858602 title "Swendsen-Wang Cuts sampling for spatially constrained Dirichlet process mixture models" @default.
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• W2090858602 doi "https://doi.org/10.1016/j.gmod.2014.03.008" @default.
• W2090858602 hasPublicationYear "2014" @default.
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