Bayesian learning, global competition and unsupervised image segmentation
文献类型：期刊论文
| 作者 | Guo, GD; Ma, SD
|
| 刊名 | PATTERN RECOGNITION LETTERS
 |
| 出版日期 | 2000-02-01 |
| 卷号 | 21期号:2页码:107-116 |
| 关键词 | Bayesian learning global competition unsupervised image segmentation Markov random field parameter estimation |
| 英文摘要 | A 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000085423600002 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9791]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.Nanyang Technol Univ, Sch Elect & Elect Engn, Intelligent Machine Res Lab, Singapore 639798, Singapore 2.CAS, NLPR, Inst Automat, Beijing 100080, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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