A Split and Merge EM Algorithm for Color Image Segmentation
As an extremely powerful probability model, Gaussian mixture model (GMM) has been widely used in the fields of pattern recognition, information processing and data mining. However, in many practical applications, the number of the components is unknown. In the case, model selection of GMM, i.e., the selection of the number of the components in the mixture, has been a rather difficult problem. Recently, the minimum message length(MML) criterion has been proposed and used to make model selection. In this paper, we propose a split and merge algorithm to decide the number of the components, which is applied to the color image segmentation. Based on MML criterion, the proposed algorithm can determine the number of components in the Gaussian mixture model automatically during the parameter learning. By splitting and merging the uncorrect components, the algorithm can converge to the maximization of the MML criterion function and get a better parameter estimation of the Gaussian mixture. It has been demonstrated well by the experiments that the proposed split and merge algorithm can make both parameter learning and model selection efficiently for color image segmentation.
Gaussian mizture model EM algorithm Model selection Split and merge operation Color image segmentation.
Yan Li Lei Li
School of Mathematical Science and Computing Technology Central South University Changsha,410075,Chi Department of Information Science School of Mathematical Sciences and LAMA Peking University Beijing
国际会议
上海
英文
2924-2928
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）