Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field

A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field (MRF) model. Due to sampling cost, Markov chain Monte Carlo (MCMC) methods are rarely used in real-time applications. Like other heuristic methods, LAE is based on a conditional independe...

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Bibliographic Details
Published in:IEEE transactions on image processing 2016-03
Main Authors: Song, Sanming, Si, Bailu, Herrmann, J Michael, Feng, Xisheng
Format: Article
Language:English
Online Access:Get full text
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Summary:A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field (MRF) model. Due to sampling cost, Markov chain Monte Carlo (MCMC) methods are rarely used in real-time applications. Like other heuristic methods, LAE is based on a conditional independence assumption. It adapts, however, the parameters in a block-by-block style with a simple Hebbian learning rule. Experiments with given label fields show that LAE is able to converge in far less time than required for a scan. It is also possible to derive an estimate for LAE based on a Cramer-Rao bound that is similar to the classical maximum pseudo-likelihood (MPL) method. As a general algorithm, LAE can be used to estimate the parameters in anisotropic label fields. Furthermore, LAE is not limited to the classical Potts model and can be applied to other types of Potts models by simple label field transformations and straightforward learning rule extensions. Experimental results on image segmentations demonstrate the efficiency and generality of the LAE algorithm.
ISSN:1941-0042