Maximum Likelihood Estimation of Gaussian Mixture Models Using PSO for Image Segmentation

2013 ◽  
Author(s):  
Khoa Anh Tran ◽  
Nhat Quang Vo ◽  
Gueesang Lee
Keyword(s):  
Image Segmentation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Likelihood Estimation ◽  
Gaussian Mixture

Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation

2011 ◽  
Vol 474-476 ◽  
pp. 442-447
Author(s):  
Zhi Gao Zeng ◽  
Li Xin Ding ◽  
Sheng Qiu Yi ◽  
San You Zeng ◽  
Zi Hua Qiu
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Mixture Models ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Image Data ◽  
Gaussian Mixture ◽  
Parameters Estimation ◽  
Video Image

In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Contains noise data, the paper presents an automatic and accurate video image segmentation algorithm, according to the spatial properties, which uses the Gaussian mixture models to segment the image. But the expectation-maximization algorithm is very sensitive to initial values, and easy to fall into local optimums, so the paper presents a differential evolution-based parameters estimation for Gaussian mixture models. The experiment result shows that the segmentation accuracy has been improved greatly than by the traditional segmentation algorithms.

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