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In image classification often occur such situations, when images in some level are corrupted by additive noise. Such noise in image classification can be modeled by Gaussian random fields (GRF). In image classification supervised and unsupervised methods are used. In this paper we compare our proposed supervised classification methods based on plugin Bayes discriminant functions (PBDF) (see  and ) with unsupervised classification method based on grey level co-occurrence matrix (GLCM)doi:10.15388/lmr.2011.mt04 fatcat:lsvpjeqlnrbc3agfb6l4jhbju4