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Vector quantization for texture classification

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1 Author(s)
G. F. McLean ; Dept. of Mech. Eng., Victoria Univ., BC, Canada

A method for classifying and coding textures that is based upon transform vector quantization is presented. Techniques for texture classification and vector quantization similarly process small, nonoverlapping blocks of image data. Local spatial frequency features have been identified as being appropriate for texture classification, indicating that a transform vector quantization scheme should be capable of characterizing and classifying textured regions. Methods for classifying codebooks using labeled training set data are presented, producing a scheme for representation and classification of texture via vector quantization. The technique is applied to a data set consisting of seven natural textures, yielding good overall performance in comparison to other texture classification schemes. Texture classification using transform vector quantization requires little supervision, provides good classification performance and utilizes a computational structure that is suitable for implementation in practical image processing systems

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:23 ,  Issue: 3 )