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Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field
Song SM(宋三明); Si BL(斯白露); Herrmann, J. Michael; Feng XS(封锡盛)
Department水下机器人研究室
Source PublicationIEEE Transactions on Image Processing
ISSN1057-7149
2016
Volume25Issue:5Pages:2324-2336
Indexed BySCI ; EI
EI Accession number20161702304103
WOS IDWOS:000374551200008
Contribution Rank1
Funding OrganizationNational 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 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.
KeywordMarkov Random Field Gibbs Distribution Parameters Estimation Local Autoencoding Potts Model
Abstract

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.

Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS KeywordIMAGE SEGMENTATION ; EM PROCEDURES ; MODEL ; ALGORITHM ; SONAR ; LIKELIHOOD ; NETWORKS ; CHAINS
WOS Research AreaComputer Science ; Engineering
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/18625
Collection水下机器人研究室
Corresponding AuthorSong SM(宋三明); Si BL(斯白露); Feng XS(封锡盛)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Institute of Perception, Action and Behavior, University of Edinburgh, Edinburgh, United Kingdom
Recommended Citation
GB/T 7714
Song SM,Si BL,Herrmann, J. Michael,et al. Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field[J]. IEEE Transactions on Image Processing,2016,25(5):2324-2336.
APA Song SM,Si BL,Herrmann, J. Michael,&Feng XS.(2016).Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field.IEEE Transactions on Image Processing,25(5),2324-2336.
MLA Song SM,et al."Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field".IEEE Transactions on Image Processing 25.5(2016):2324-2336.
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