|Title||Adaptive color image segmentation using Markov random fields|
|Publication Type||Conference Paper|
|Year of Publication||2002|
|Authors||Wesolkowski, S., and P. Fieguth|
|Conference Name||International Conference on Pattern Recognition|
|Conference Location||Rochester, NY|
|Keywords||adaptive color image segmentation, adaptive signal processing, continuous Gibbs sampler, continuous-valued regional prototypes, discrete Gibbs sampler, global spatial constraints, image colour analysis, image sampling, image segmentation, local spatial constraints, Markov processes, Markov Random Fields, point-based methods, random processes, region boundaries adjustment, spatially-based methods, vector quantization|
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.