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Expectation maximization method for parameter estimation of image statistical model
图像统计模型参数估计中的期望最大值算法

Keywords: expectation maximization algorithm,image statistical model,parameter estimation,evolution algorithm
期望最大值算法
,图像统计模型,参数估计,进化算法

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Abstract:

Expectation maximization (EM)algorithm for parameter estimation of image statistical model is one of the striking research fields in recent decades.Based on the analysis of the EM algorithm,combining the current application research in parameter estimation of image statistical model,analysis and comparison are conducted in terms of the three improvement schemes of standard EM algorithm.In this paper,integrating image restoration,segmentation,object tracking and the fusion of other evolution optimization algorithms,through three aspects,such as the selection of missing data sets,the statistical model establishments of missing and incomplete data sets,and parameter estimation of image statistical models,as well as the advantages and disadvantages of the corresponding EM algorithm are exponded.The structure and complexity of EM algorithm,so far as to success or failure,are directly determined by the selection of missing data and the expression form of incomplete data.In the end,challenges and possible trends are discussed,and extensive applications of EM algorithm to parameter estimation of statistical model with missing data are pointed out.

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