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题名: Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field
作者: Song SM(宋三明); Si BL(斯白露); Herrmann, J. Michael; Feng XS(封锡盛)
作者部门: 海洋信息技术装备中心
关键词: Markov random field ; Gibbs distribution ; parameters estimation ; local autoencoding ; Potts model
刊名: IEEE Transactions on Image Processing
ISSN号: 1057-7149
出版日期: 2016
卷号: 25, 期号:5, 页码:2324-2336
收录类别: SCI ; EI
产权排序: 1
项目资助者: 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记录号: WOS:000374551200008
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
关键词[WOS]: IMAGE SEGMENTATION ; EM PROCEDURES ; MODEL ; ALGORITHM ; SONAR ; LIKELIHOOD ; NETWORKS ; CHAINS
研究领域[WOS]: Computer Science ; Engineering
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/18625
Appears in Collections:海洋信息技术装备中心_期刊论文

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Recommended Citation:
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.
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文件名: Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field.pdf
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