Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7205
Title: A spatially-constrained normalized gamma process for data clustering
Authors: Korkinof, Dimitrios 
Demiris, Yiannis 
Chatzis, Sotirios P. 
Korkinof, Dimitrios 
Demiris, Yiannis 
Keywords: Information systems;Artificial intelligence;Markov random fields
Category: Mechanical Engineering
Field: Engineering and Technology
Issue Date: 2012
Publisher: Springer
Source: Artificial intelligence applications and innovations: 8th IFIP WG 12.5 international conference, AIAI 2012, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part I, Pages 337-346
Abstract: In this work, we propose a novel nonparametric Bayesian method for clustering of data with spatial interdependencies. Specifically, we devise a novel normalized Gamma process, regulated by a simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. As a result of its construction, the proposed model allows for introducing spatial dependencies in the clustering mechanics of the normalized Gamma process, thus yielding a novel nonparametric Bayesian method for spatial data clustering. We derive an efficient truncated variational Bayesian algorithm for model inference. We examine the efficacy of our approach by considering an image segmentation application using a real-world dataset. We show that our approach outperforms related methods from the field of Bayesian nonparametrics, including the infinite hidden Markov random field model, and the Dirichlet process prior
URI: http://ktisis.cut.ac.cy/handle/10488/7205
ISBN: 978-3-642-33408-5 (print)
978-3-642-33409-2 (online)
DOI: 10.1007/978-3-642-33409-2_35
Rights: © 2012 IFIP International Federation for Information Processing
Type: Book Chapter
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

Show full item record

SCOPUSTM   
Citations 50

1
checked on Nov 16, 2017

Page view(s) 50

29
Last Week
1
Last month
2
checked on Nov 20, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.