Relaxation Labeling of Markov Random Fields

Using Markov random eld (MRF) theory, a variety of computer vision problems can be modeled in terms of optimization based on the maximum a poste-riori (MAP) criterion. The MAP connguration minimizes the energy of a posterior (Gibbs) distribution. When the label set is discrete, the minimization is combinatorial. This paper proposes to use the continuous relaxation labeling (RL) method for the minimization. The RL converts the original NP complete problem into one of polynomial complexity. Annealing may be combined into the RL process to improve the quality (globalness) of RL solutions. Performance comparison among four diierent RL algorithms is given.