01830nas a2200157 4500008004100000245008400041210006900125260003000194300001400224520122500238100001701463700002101480700001801501700002901519856012401548 1995 eng d00aUnsupervised parallel image classification using a hierarchical Markovian model0 aUnsupervised parallel image classification using a hierarchical aPiscatawaybIEEEc1995/// a169 - 1743 aThis 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).1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Marc1 a*Society, IEEE, Computer uhttps://www.inf.u-szeged.hu/publication/unsupervised-parallel-image-classification-using-a-hierarchical-markovian-model00664nas a2200145 4500008004100000245011900041210006900160260003200229300001400261100002100275700001700296700001800313700002900331856015800360 1994 eng d00aMulti-Temperature Annealing: A New Approach for the Energy-Minimization of Hierarchical Markov Random Field Models0 aMultiTemperature Annealing A New Approach for the EnergyMinimiza aLos AlamitosbIEEEc1994/// a520 - 5221 aZerubia, Josiane1 aKato, Zoltan1 aBerthod, Mark1 a*Society, IEEE, Computer uhttps://www.inf.u-szeged.hu/publication/multi-temperature-annealing-a-new-approach-for-the-energy-minimization-of-hierarchical-markov-random-field-models