Supervised color image segmentation using constrained compound MRF model with Bi-level line Field
Sucheta Panda, Pradipta Kumar Nanda · 2011
In this paper, we propose an supervised 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. 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.