NDLI: A sequence-based generalization of mean-field annealing using the Forward/Backward algorithm: Application to image segmentation

Content Provider	IEEE Xplore Digital Library
Author	Miller, David J. Bunyaratavej, Piya Zhao, Qi
Copyright Year	2002
Description	Author affiliation: Dept. of Elec. Eng., The Pennsylvania State University, 227-C EEW, University Park, 16802, USA (Miller, David J.; Bunyaratavej, Piya; Zhao, Qi)
Abstract	Mean-field annealing (MFA) is widely used for optimization tasks involving the determination of a set of discrete-valued assignment variables. One way of deriving MFA is via maximum entropy (ME), where one seeks the joint distribution over the (random) assignments subject to an average level of cost. MFA is obtained by assuming the individual assignments are independent. Here we propose an MFA extension for problems defined on the pixel sites of an image. Rather than introducing variables for individual sites, we represent label choices for an entire image row (or column). We then make the less restrictive assumption of independent row (rather than pixel) labelings. While it is not possible to explicitly evaluate the row labeling distribution, we can, via a Forward/Backward algorithm, explicitly evaluate sums over this distribution, to obtain a posteriori probabilities at individual sites. It turns out that the site probabilities, in turn, determine (updated) row labeling probabilities. Thus, the Forward/Backward algorithm forms the basis of an iteration, applied to the rows(columns) of the image, that yields optimized a posteriori site probabilities. This iterative method descends in the ME Lagrangian/free energy. Our method was applied to segmentation of synthetic, noise-corrupted Markov random field images. It achieved substantial reduction in misclassification rates, compared with both ICM and standard MFA.
File Size	1941366
File Format	PDF
ISBN	0780374029
ISSN	15206149
DOI	10.1109/ICASSP.2002.5743955
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Pixel
Content Type	Text
Resource Type	Article

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