Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7199
Title: A markov random field-regulated pitman-yor process prior for spatially constrained data clustering
Authors: Chatzis, Sotirios P. 
Keywords: Pattern recognition;Markov random fields;Computer science
Category: Electrical Engineering, Electronic Engineering, Information Engineering
Field: Engineering and Technology
Issue Date: 2013
Publisher: Elsevier
Source: Pattern recognition, 2013, Volume 46, Issue 6, Pages 1595–1603
Abstract: In this work, we propose a Markov random field-regulated Pitman–Yor process (MRF-PYP) prior for nonparametric clustering of data with spatial interdependencies. The MRF-PYP is constructed by imposing a Pitman–Yor process over the distribution of the latent variables that allocate data points to clusters (model states), the discount hyperparameter of which is regulated by an additionally postulated simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. Further, based on the stick-breaking construction of the Pitman–Yor process, we derive an efficient truncated variational Bayesian algorithm for model inference. We examine the efficacy of our approach by considering an unsupervised image segmentation application using a real-world dataset. We show that our approach completely outperforms related methods from the field of Bayesian nonparametrics, including the recently proposed infinite hidden Markov random field model and the Dirichlet process prior
URI: http://ktisis.cut.ac.cy/handle/10488/7199
ISSN: 0031-3203
DOI: http://dx.doi.org/10.1016/j.patcog.2012.11.026
Rights: © 2012 Elsevier Ltd. All rights reserved
Type: Article
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