TEXTURE CLASSIFICATION BASED ON SPATIAL DEPENDENCE FEATURES USING CO-OCCURRENCE MATRICES AND MARKOV RANDOM FIELDS (MA-P1)

Author(s) : William Schwartz (Federal University of Parana - Computer Science Department, Brazil) Hélio Pedrini (Federal University of Parana - Computer Science Department, Brazil)

Abstract : This paper presents a method for unsupervised classification of textures based on features obtained from co-occurrence matrices and Markov Random Fields. Two steps are performed to classify the images. Initially, the method recognizes the homogeneous regions (object interior) in the image. Regions consisting of dissimilar elements (transition between objects) are then properly identified and classified. Experimental results demonstrate the robustness of the method in terms of variation in region size and number of parameters.

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