In this paper, we present two relaxation techniques : deterministic pseudo-annealing (DPA) and modified metropolis dynamics (MMD) in order to do image classification using a Markov random field modelization. For the first algorithm (DPA), the a posteriori probability of a tentative labeling is generalized to continuous labeling. The merit function thus defined has the same maxima under constraints yielding probability vectors. Changing these constraints convexify the merit function. The algorithm solve this unambigous maximization problem and then tracks down the solution while the original constraints are restored yielding a good even if suboptimal solution to the original labeling assignment problem. As for the second method (MMD) it is a modified version of the metropolis algorithm : at each iteration the new state is chosen randomly but the decision to accept it is purely deterministic. This is of course also a suboptimal technique which gives faster results than stochastic relaxation. These two methods have been implemented on a connection machine CM2 and simulation results are shown with a synthetic noisy image and a SPOT image. These results are compared to those obtained with the metropolis algorithm, the Gibbs sampler and ICM (Iterated Conditional Mode).