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 model. Due to sampling cost, Markov chain Monte Carlo methods are rarely used in real-time applications. Like other heuristic methods, LAE is based on a conditional independence assumptio...

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Bibliographic Details
Published in:IEEE transactions on image processing 2016-05, Vol.25 (5), p.2324-2336
Main Authors: Sanming Song, Bailu Si, Herrmann, J. Michael, Xisheng Feng
Format: Article
Language:English
Subjects:
Algorithms
Computational modeling
Estimates
Estimation
Fields (mathematics)
Gibbs distribution
Image coding
Image segmentation
Labels
Learning
local autoencoding
Markov analysis
Markov processes
Markov random field
Mathematical models
Monte Carlo methods
Monte Carlo simulation
Parameter estimation
parameters estimation
Potts model
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Summary:A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field model. Due to sampling cost, Markov chain Monte Carlo 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 the 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 pseudolikelihood 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:1057-7149
1941-0042
DOI:10.1109/TIP.2016.2545299