Unsupervised parallel image classification using a hierarchical Markovian model

The paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the only observable image. Our approach consists of extending a recent iterative method of estimation, called iterative conditional estimation (ICE) to a hierarchical Markovian model. The idea resembles the estimation-maximization (EM) algorithm as we recursively look at the maximum a posteriori (MAP) estimate of the label field given the estimated parameters then we look at the maximum likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. We propose unsupervised image classification algorithms using a hierarchical model. The only parameter supposed to be known is the number of regions, all the other parameters are estimated. The presented algorithms have been implemented on a Connection Machine CM200. Comparative tests have been done on noisy synthetic and real images (remote sensing)