This paper presents a method for applying inductive learning techniques to texture description. Local features of texture described as eight attributes have been extracted for each pixel from small w:ndows (5x5, 7x7 or 9x9) centered around the pixel and extra ninth attribute is computed from larger global area (25*25) as co-occurrence matrix parameter. All nine attributes form an event, which is essentially a point in a 9-dimensional attribute space. Sets of such events are computed for different texture classes, and the inductive learning AQ algorithm is used to generate a given class description. Such learned descriptions are evaluated using new qifferent texture samples. Results of experiments performed on eight textural images are presented
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