Adaptive color image segmentation using Markov random fields

A new framework for color image segmentation is introduced generalizing the concepts of point-based and spatially-based methods. This framework is based on Markov Random Fields using a Continuous Gibbs Sampler. The Markov Random Fields approach allows for a rigorous computational framework where local and global spatial constraints can be globally optimized. Using a Continuous Gibbs Sampler enables the algorithm to adapt continuous-valued regional prototypes in a manner analogous to vector quantization while the discrete Gibbs Sampler is used to adjust region boundaries.