Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation

	Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation
	Li Bo 1,2,3; Liu Wenju3 ; Dou Lihua 1,2
发表期刊	CHINESE JOURNAL OF ELECTRONICS
	2010-07-01
卷号	19 期号:3 页码:451-456
文章类型	Article
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.
关键词	Gaussian Mixture Model Model Order Degenerating Model Elliptically Contoured Distributions Image Segmentation
WOS标题词	Science & Technology ; Technology
关键词[WOS]	KOTZ-TYPE DISTRIBUTION
收录类别	SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000280461900014
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/2970
专题	多模态人工智能系统全国重点实验室_机器人视觉
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
第一作者单位	模式识别国家重点实验室
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.