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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)
作者部门水下机器人研究室
关键词Markov Random Field Gibbs Distribution Parameters Estimation Local Autoencoding Potts Model
发表期刊IEEE Transactions on Image Processing
ISSN1057-7149
2016
卷号25期号:5页码:2324-2336
收录类别SCI ; EI
EI收录号20161702304103
WOS记录号WOS:000374551200008
产权排序1
资助机构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.
摘要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.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
关键词[WOS]IMAGE SEGMENTATION ; EM PROCEDURES ; MODEL ; ALGORITHM ; SONAR ; LIKELIHOOD ; NETWORKS ; CHAINS
WOS研究方向Computer Science ; Engineering
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/18625
专题水下机器人研究室
通讯作者Song SM(宋三明); Si BL(斯白露); Feng XS(封锡盛)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Institute of Perception, Action and Behavior, University of Edinburgh, Edinburgh, United Kingdom
推荐引用方式
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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