Novel cluster-based probability model for texture synthesis, classification, and compression

Kris Popat; Rosalind W. Picard

doi:10.1117/12.157992

22 October 1993 Novel cluster-based probability model for texture synthesis, classification, and compression

Kris Popat, Rosalind W. Picard

Proceedings Volume 2094, Visual Communications and Image Processing '93; (1993) https://doi.org/10.1117/12.157992
Event: Visual Communications and Image Processing '93, 1993, Cambridge, MA, United States

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.

Citation Download Citation

Kris Popat and Rosalind W. Picard "Novel cluster-based probability model for texture synthesis, classification, and compression", Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); https://doi.org/10.1117/12.157992

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