Color image segmentation using Markov random fields

The use of Markov random fields (MRFs) in color image segmentation of natural outdoor scenes is discussed. MRFs provide an elegant means of specifying a local energy function which embodies the expected dependencies of neighboring pixels and includes both the prior and posterior probabilistic distributions. This local neighborhood-based specification of dependencies avoids ad hoc brittle methods using global image knowledge. A brief analysis of ongoing research in color differencing methods is presented, since they are central to the problem of color segmentation. The authors develop and compare the use of three different lattice structures for coupled MRFs with line and color processes based on squares, hexagons, and triangles, and also discusses current efforts in MRF parameter understanding