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Proceedings Paper

Texture classification using transform vector quantization
Author(s): Gerard F. McLean
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Paper Abstract

This paper presents a method for the classification and coding of textures based upon the use of transform vector quantization. Techniques for texture classification and vector quantization similarly process small, nonoverlapping blocks of image data which are extracted independently from the image. 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. A data set consisting of 7 natural textures is used to demonstrate the utility of this approach. The experimental results show acceptable classification rates and suggest avenuws for future research which will yield significant improvements in future work.

Paper Details

Date Published: 1 September 1990
PDF: 12 pages
Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); doi: 10.1117/12.24148
Show Author Affiliations
Gerard F. McLean, Univ. of Victoria (Canada)

Published in SPIE Proceedings Vol. 1360:
Visual Communications and Image Processing '90: Fifth in a Series
Murat Kunt, Editor(s)

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