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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1249

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contributor.authorPanda, S-
contributor.authorNanda, P K-
date.accessioned2010-04-30T09:42:40Z-
date.available2010-04-30T09:42:40Z-
date.issued2009-
identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 5909 LNCS, 2009, Pages 291-296en
identifier.urihttp://dx.doi.org/10.1007/978-3-642-11164-8_47-
identifier.urihttp://hdl.handle.net/2080/1249-
description.abstractIn this paper, we propose an unsupervised color image segmentation scheme using homotopy continuation method and Compound Markov Random Field (CMRF) model. The proposed scheme is recursive in nature where model parameter estimation and the image label estimation are alternated. Ohta (I 1, I 2, I 3) model is used as the color model for image segmentation and we propose a compound MRF model taking care of intra-color and inter-color plane interactions. The CMRF model parameters are estimated using Maximum Conditional Pseudo Likelihood (MCPL) criterion and the MCPL estimates are obtained using homotopy continuation method. The image label estimation is formulated using Maximum a Posteriori criterion and the MAP estimates are obtained using hybrid algorithm. In the context of misclassification error, the proposed unsupervised scheme with CMRF model exhibited improved segmentation accuracy as compared to MRF model and Kato's method. © 2009 Springer-Verlag Berlin Heidelberg.en
format.extent878453 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherSpringerLinken
subjectColor Image;en
subjectColor Model;en
subjectMRF model;en
subjectSegmentation;en
subjectSimulated Annealingen
titleUnsupervised Color Image Segmentation using Compound Markov Random Field Modelen
typeArticleen
Appears in Collections:Conference Papers

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