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Proceedings Paper

Novel cluster-based probability model for texture synthesis, classification, and compression
Author(s): Kris Popat; Rosalind W. Picard
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Paper Abstract

We present a new probabilistic modeling technique for high-dimensional vector sources, and consider its application to the problems of texture synthesis, classification, and compression. Our model combines kernel estimation with clustering, to obtain a semiparametric probability mass function estimate which summarizes -- rather than contains -- the training data. Because the model is cluster based, it is inferable from a limited set of training data, despite the model's high dimensionality. Moreover, its functional form allows recursive implementation that avoids exponential growth in required memory as the number of dimensions increases. Experimental results are presented for each of the three applications considered.

Paper Details

Date Published: 22 October 1993
PDF: 13 pages
Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); doi: 10.1117/12.157992
Show Author Affiliations
Kris Popat, Media Lab./MIT (United States)
Rosalind W. Picard, Media Lab./MIT (United States)

Published in SPIE Proceedings Vol. 2094:
Visual Communications and Image Processing '93
Barry G. Haskell; Hsueh-Ming Hang, Editor(s)

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