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图像线状模式的有限混合模型及其EM算法 被引量：12

The Finite Mixture Model and Its EM Algorithm for Line-Type Image Patterns
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摘要 针对能够用回归模型刻画的图像特征,提出一个有限混合识别模型.该模型由有限个回归类构成,每个类的模型误差可以是正态的,也可以是满足一定条件的任意分布.文中给出了估计线性回归类参数的EM算法,该算法可推广到高维情形. A finite mixture model for identification is presented in order to recognize image patterns which can be characterized by regression models. The model consists of a finite number of regression classes, for each of them the model error may normal or arbitrary under certain conditions. The EM algorithm is constructed to estimate parameters of linear regression classes, and it can be generalized to high dimension cases.
作者 马江洪 葛咏
机构地区 陕西师范大学数学与信息科学学院 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室
出处 《计算机学报》 EI CSCD 北大核心 2007年第2期288-296,共9页 Chinese Journal of Computers
基金 中国博士后基金(2005037246) 中国科学院前沿创新研究项目基金(V36400) 中国科学院遥感研究所资助 北京师范大学遥感科学国家重点实验室开放课题(LRSS0610)资助~~
关键词 回归类 混合模型 EM算法 模式识别 regression class mixture model EM algorithm pattern recognition
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献7

  • 1McLachlan G J,Basford K E.Mixture Models:Inference and Applications to Clustering.New York:Marcel Dekker,1988
  • 2Sclove S C.Application of the conditional population mixture model to image segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence,1983,PAMI-5:428-433
  • 3Zhuang X et al.Gaussian mixture density modeling,decomposition,and applications.IEEE Transactions on Image Processing,1996,5(9):1293-1302
  • 4McLachlan G J,Krishnan T.The EM Algorithm and Extensions.John Wiley,1997
  • 5Leung Y,Ma J H,Zhang W X.A new method for mining regression classes in large data sets.IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(1):5-21
  • 6马江洪,张文修,梁怡.挖掘回归类的混合模型的可识别性[J].计算机学报,2003,26(12):1652-1659. 被引量：2
  • 7Amari S.Information geometry of the EM and em algorithms for neural networks.Neural Networks,1995,8(9):1379-1408

二级参考文献13

  • 1[1]Fayyad U,Stolorz P. Data mining and KDD: Promise and challenges. Future Generation Computer Systems, 1997, 13(2-3):99～115
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  • 4[5]McLachlan G J, Basford K E. Mixture Models: Inference and Applications to Clustering. New York: Marcel Dekker, 1988
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  • 10[11]Prakasa Rao B L S. Identifiability in Stochastic Models: Characterizations of Probability Distributions. Boston: Academic Press, 1992

共引文献1

同被引文献250

引证文献12

二级引证文献122

计算机学报
2007年 第2期

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