National Natural Science Foundation of China under Grant 41506121, in part by the China Post-Doctoral Science Foundation under Grant 2014M561266, in part by the Jiang Xinsong Innovation Fund under Grant Y4FC012901, in part by the State Key Laboratory of Robotics under Grant Y5A1203901, in part by the Distinguished Young Scholar Project of the Thousand Talents Program of China under Grant Y5A1370101, in part by the Doctoral Scientific Research Foundation of Liaoning Province under Grant 201501035, and in part by the Project Research and Development Center for Underwater Construction Robotics within the Ministry of Ocean and Fisheries through the Korea Institute of Marine Science and Technology Promotion, Korea, under Grant PJT200539.
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.