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

• Published in: IEEE transactions on image processing Vol. 25; no. 5; pp. 2324 - 2336
• Main Authors: Sanming Song, Bailu Si, Herrmann, J. Michael, Xisheng Feng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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; Gibbs distribution; parameters estimation; Markov random field; Potts model; local autoencoding
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2016.2545299

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.