Unsupervised Color Image Segmentation Using Constrained Compound MRF Model with Bi-level Line Field
Sucheta Panda, Pradipta Kumar Nanda · 2010
In this paper, we propose an unsupervised color image segmentation scheme using homotopy continuation method and Compound Markov Random Field (CMRF) model with Bilevel Binary Line Fields. The scheme is specifically meant to preserve weak edges besides the well defined strong edges. 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 Yu and Clausi 's method.