BAYESIAN CONTEXTUAL CLASSIFICATION BASED ON MODIFIED M-ESTIMATES AND MARKOV RANDOM-FIELDS

A Bayesian contextual classification scheme is presented in connection with modified M-estimates and a discrete Markov random field model, T he spatial dependency of adjacent class labels is characterized based on local transition probabilities in order to use contextual informati on. Due to the computational load required to estimate class labels in the final stage of optimization and the need to acquire robust spectr al attributes derived from the training samples, modified M-estimates are implemented to characterize the joint class-conditional distributi on, The experimental results show that the suggested scheme outperform s conventional noncontextual classifiers as well as contextual classif iers which are based on least squares estimates or other spatial inter action models.