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Bayesian learning, global competition and unsupervised image segmentation
Guo, GD; Ma, SD
Source PublicationPATTERN RECOGNITION LETTERS
2000-02-01
Volume21Issue:2Pages:107-116
SubtypeArticle
AbstractA novel approach to unsupervised stochastic model-based image segmentation is presented and the problems of parameter estimation and image segmentation are formulated as Bayesian learning. In order to draw samples corresponding to different classes, a global competition strategy is adopted for label commitment based on the "powervalue" (PV) associated with each sample (or site). The smaller the value, the more powerful the sample to compete. Parameter estimation and image segmentation are executed in the same process. Bayesian modeling of images by Markov random fields (MRFs) makes it easy to represent the power of each site for competition. The new procedure to unsupervised image segmentation is performed on synthetic and real images to show its success. (C) 2000 Elsevier Science B.V. All rights reserved.
KeywordBayesian Learning Global Competition Unsupervised Image Segmentation Markov Random Field Parameter Estimation
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000085423600002
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9791
Collection09年以前成果
Affiliation1.Nanyang Technol Univ, Sch Elect & Elect Engn, Intelligent Machine Res Lab, Singapore 639798, Singapore
2.CAS, NLPR, Inst Automat, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Guo, GD,Ma, SD. Bayesian learning, global competition and unsupervised image segmentation[J]. PATTERN RECOGNITION LETTERS,2000,21(2):107-116.
APA Guo, GD,&Ma, SD.(2000).Bayesian learning, global competition and unsupervised image segmentation.PATTERN RECOGNITION LETTERS,21(2),107-116.
MLA Guo, GD,et al."Bayesian learning, global competition and unsupervised image segmentation".PATTERN RECOGNITION LETTERS 21.2(2000):107-116.
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