A spatially constrained asymmetric Gaussian mixture model for image segmentation

2021 ◽  
Author(s):  
Yunjie Chen ◽  
Ning Cheng ◽  
Mao Cai ◽  
Chunzheng Cao ◽  
Jianwei Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture

Multispectral image segmentation by fuzzy clustering algorithm used Gaussian mixture model

2017 ◽  
Vol 25 (2) ◽  
pp. 509-518 ◽  
Author(s):  
李 玉 LI Yu ◽  
徐 艳 XU Yan ◽  
赵雪梅 ZHAO Xue-mei ◽  
赵泉华 ZHAO Quan-hua
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Gaussian Mixture ◽  
Multispectral Image ◽  
Fuzzy Clustering Algorithm

scholarly journals Accurate image segmentation using Gaussian mixture model with saliency map

2017 ◽  
Vol 21 (3) ◽  
pp. 869-878 ◽  
Author(s):  
Hui Bi ◽  
Hui Tang ◽  
Guanyu Yang ◽  
Huazhong Shu ◽  
Jean-Louis Dillenseger
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Saliency Map

Traffic Image Segmentation Based on Gaussian Mixture Model with Spatial Information and Sampling

2013 ◽  
Vol 380-384 ◽  
pp. 3702-3705
Author(s):  
Xiao Na Zhang ◽  
Ming Yao ◽  
Feng Zhu ◽  
Jie Ni
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Spatial Information ◽  
Gaussian Mixture ◽  
Sampling Theorem ◽  
Information Function ◽  
Cluster Number ◽  
Minimum Sample ◽  
Image Pixels

The application of classical gaussian mixture model to image segmentation has highly computer complexiton and have not taking into account spatial information except intensity values. A image segmentation based on Gaussian mixture model with sampling and spatially information is proposed in order to solve this problem. First, a spatial information function is defined as the neighbour information weighted class probabilities of very pixels; Secondly, the sampling theorem is given in this paper,and the size of the minimum sample has been derived according to the smallest cluster and cluster number; Finally, image pixels are sampled based on the size of the minimum sample to estimate the parameter of model , which are classifed to different clusters according to bayesian rules. The experimental results show the effectiveness of the algorithm.

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