Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation
文献类型：期刊论文
| 作者 | Li Bo1,2,3; Liu Wenju3 ; Dou Lihua1,2
|
| 刊名 | CHINESE JOURNAL OF ELECTRONICS
 |
| 出版日期 | 2010-07-01 |
| 卷号 | 19期号:3页码:451-456 |
| 关键词 | Gaussian mixture model Model order Degenerating model Elliptically contoured distributions Image segmentation |
| 英文摘要 | Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling. Finite Gaussian mixture model is usually used in practice and the selection of number of mixture components is a significant problem in its application. For example, in image segmentation, it is the donation of the number of segmentation regions. The determination of the optimal model order therefore is a problem that achieves widely attention. This paper proposes a degenerating model algorithm that could simultaneously select the optimal number of mixture components and estimate the parameters for Gaussian mixture model. Unlike traditional model order selection method, it does not need to select the optimal number of components from a set of candidate models. Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it select the correct model order in a different way that needs less operation times and less sensitive to the initial value of EM. The experimental results show the effectiveness of the algorithm. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Engineering, Electrical & Electronic |
| 研究领域[WOS] | Engineering |
| 关键词[WOS] | KOTZ-TYPE DISTRIBUTION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000280461900014 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/2970]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 |
| 作者单位 | 1.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China 2.Beijing Inst Technol, Key Lab Complex Syst Intelligent Control & Decis, Minist Educ, Beijing 100081, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li Bo,Liu Wenju,Dou Lihua. Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation[J]. CHINESE JOURNAL OF ELECTRONICS,2010,19(3):451-456. |
| APA | Li Bo,Liu Wenju,&Dou Lihua.(2010).Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation.CHINESE JOURNAL OF ELECTRONICS,19(3),451-456. |
| MLA | Li Bo,et al."Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation".CHINESE JOURNAL OF ELECTRONICS 19.3(2010):451-456. |
入库方式： OAI收割
来源：自动化研究所
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