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dc.contributor.authorPanda, S-
dc.contributor.authorNanda, P K-
dc.contributor.authorDey, R-
dc.identifier.citationIEEE Region 10 Conference TENCON, Hyderabad, November 19-21, 2008en
dc.description.abstractIn this paper, we propose a constrained compound Markov random field model (MRF) to model color texture as well as scene images. Ohta (I1, I2, I3) color model is used as the color model for segmentation. Besides, intra plane model, the constrained model is modified to take care of inter-plane interaction as well. Hence, the model is called as double constrained compound MRF (DCCMRF) model. The problem is formulated as pixel labelling problem and the pixel labels are estimated using maximum a posteriori (MAP) criterion.The MAP estimates are obtained using hybrid algorithm. The DCCMRF model exhibited improved segmentation accuracy as compared to DCMRF, MRF, double MRF (DMRF), double Gauss MRF(DGMRF) and JSEG method. The proposed models have been successfully tested for two, four and five class problem.en
dc.format.extent506137 bytes-
dc.subjectGaussian processesen
dc.subjectMarkov processesen
dc.subjectimage colour analysisen
dc.subjectimage segmentationen
dc.subjectimage textureen
dc.subjectmaximum likelihood estimationen
dc.titleConstrained Compound Markov Random Field Model for Segmentation of Color Texture and Scene Imagesen
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