BYY harmony learning of t-mixtures with the application to image segmentation based on contourlet texture features

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• 作者：Yunsheng JiangAuthor Vitae ; Chenglin LiuAuthor Vitae ; Jinwen Ma ; ^{jwma@math.pku.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Bayesian Ying-Yang (BYY) harmony learning ; Multivariate t-mixture ; Gradient learning ; Model selection ; Contourlet texture features
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 May 2016
• 年：2016
• 卷：188
• 期：Complete
• 页码：262-274
• 全文大小：9539 K

文摘

In this paper, we extend Bayesian Ying-Yang (BYY) harmony learning to the case of multivariate t-mixtures and propose a gradient BYY harmony learning algorithm that can automatically determine the number of actual t-distributions in a dataset during parameter learning. It is demonstrated by simulation experiments that this proposed algorithm for t-mixtures is both effective and stable on model selection and parameter estimation. Moreover, by mainly utilizing certain contourlet texture features from an image, the proposed algorithm is successfully applied to unsupervised image segmentation, showing considerable advantages for both general and multi-texture images.