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Stochastic grammars for images on arbitrary graphs

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4 Author(s)
J. M. Siskind ; Purdue Univ., CA, USA ; I. Pollak ; P. Harper ; Bouman

We describe a class of multiscale stochastic processes based on stochastic context-free grammars and called spatial random trees (SRTs) which can be effectively used for modeling multidimensional signals. In addition to modeling images which are sampled on a regular rectangular grid, we generalize this methodology to images defined on arbitrary graph structures. We develop likelihood calculation, MAP estimation, and EM-based parameter estimation algorithms for SRTs. To illustrate these methods, we apply them to classification of natural images using region graphs extracted by a recursive bipartitioning segmentation algorithm.

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003