Segmentation algorithms are usually qualified of supervised or non-supervised according to the amount of external information needed during the procedure. This article will list several examples of Markovian supervised or non supervised segmentation algorithms in order to present several modeling possibilities and ways to improve the initial models. Following a Bayesian approach, the energies are usually divided in two terms: the interaction term and the regularization term. After introducing the two basical models, we will compare the two energies, discuss more precisely of the different terms and more precisely, of the interaction term. Then the neighborhood systems will be considered as well as their possible dependency on the observations. We will also present general ways to use some edge information in the segmentation energies and a more general segmentation approach allowing the use of `non-classified' labels. Finally, various hierarchical approaches can be used in order to alleviate the optimization task and for different kind of energies. We are not mainly interested here in ways to improve the optimization procedure but rather in the definition of new models for non supervised segmentation. They will combine different primitives and the usual segmentation energies.
"Stochastic modeling in image segmentation", Proc. SPIE 3457, Mathematical Modeling and Estimation Techniques in Computer Vision, (24 September 1998); doi: 10.1117/12.323450; https://doi.org/10.1117/12.323450