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An unsupervised retraining technique for a maximum likelihood (ML) classifier is presented. The proposed technique allows the classifier's parameters, obtained by supervised learning on a specific image, to be updated in a totally unsupervised way on the basis of the distribution of a new image to be classified. This enables the classifier to provide a high accuracy for the new image even when the corresponding training set is not available. Index Terms-Expectation maximization algorithm,doi:10.1109/36.905255 fatcat:yeoygalcw5d4dkh55zu3sval4e