Unsupervised Color Image Segmentation Using Compound Markov Random Field Model

  • Sucheta Panda
  • P. K. Nanda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

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 (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.

Keywords

Color Image Color Model Segmentation Simulated Annealing MRF model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sucheta Panda
    • 1
  • P. K. Nanda
    • 2
  1. 1.IPCV Lab., Department of Electrical EngineeringNational Institute of TechnologyRourkelaIndia
  2. 2.Department of Electronics and Telecommunication EngineeringC.V Raman College of EngineeringBhubaneswarIndia

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