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PREMI
2009
Springer

Unsupervised Color Image Segmentation Using Compound Markov Random Field Model

9 years 7 months ago
Unsupervised Color Image Segmentation Using Compound Markov Random Field Model
Abstract. In 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 (I1, I2, I3) 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. Key words: Color Image,Color Model,Segmentation,...
Sucheta Panda, P. K. Nanda
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where PREMI
Authors Sucheta Panda, P. K. Nanda
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