Unsupervised Image Segmentation Based on Expectation-Maximization Algorithm

被引：0
|
作者：
Guan, Ji-shi [1 ,2 ]
Shi, Yao-wu [1 ]
Qiu, Jian-wen [2 ]
Hou, Yi-min [3 ]
机构：
[1] Jilin Univ, Sch Commun Engn, Jilin 130012, Peoples R China
[2] China Nucl Power Technol Res Inst Beijing Div, Beijing 100086, Peoples R China
[3] Northeast Dianli Univ, Sch Automat Engn, Jilin 132012, Peoples R China
来源：
2015 INTERNATIONAL CONFERENCE ON APPLIED MECHANICS AND MECHATRONICS ENGINEERING (AMME 2015) | 2015年
关键词：
Expectation-maximization; Bayesian information criterion; Image segmentation;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The paper proposes a novel image segmentation method based on Expectation-Maximization and Bayesian Information Criterion. The Expectation-Maximization theory is used to estimate the data distribution of the input image firstly. The number of class is calculated by Bayesian Information Criterion. The Maximum Likelihood is employed to classify the image pixels into the nearest class. The excellence of the proposed method is being independent to original estimate and it can be used in unsupervised image segmentation. The real images are used in the experiment. The results show that this method is efficient.
引用
收藏
页码：506 / 510
页数：5
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