A finite mixture model based on pair-copula construction of multivariate distributions and its application to color image segmentation

2012 ◽  
Author(s):  
Anandarup Roy ◽  
Swapan K. Parui ◽  
Utpal Roy
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Color Image ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Color Image Segmentation ◽  
Multivariate Distributions ◽  
Model Based

scholarly journals Image Segmentation Using a Trimmed Likelihood Estimator in the Asymmetric Mixture Model Based on Generalized Gamma and Gaussian Distributions

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Yi Zhou ◽  
Hongqing Zhu
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Superior Performance ◽  
Observation Data ◽  
Likelihood Estimator ◽  
Gaussian Distributions ◽  
Model Based ◽  
Generalized Gamma

Finite mixture model (FMM) is being increasingly used for unsupervised image segmentation. In this paper, a new finite mixture model based on a combination of generalized Gamma and Gaussian distributions using a trimmed likelihood estimator (GGMM-TLE) is proposed. GGMM-TLE combines the effectiveness of Gaussian distribution with the asymmetric capability of generalized Gamma distribution to provide superior flexibility for describing different shapes of observation data. Another advantage is that we consider the spatial information among neighbouring pixels by introducing Markov random field (MRF); thus, the proposed mixture model remains sufficiently robust with respect to different types and levels of noise. Moreover, this paper presents a new component-based confidence level ordering trimmed likelihood estimator, with a simple form, allowing GGMM-TLE to estimate the parameters after discarding the outliers. Thus, the proposed algorithm can effectively eliminate the disturbance of outliers. Furthermore, the paper proves the identifiability of the proposed mixture model in theory to guarantee that the parameter estimation procedures are well defined. Finally, an expectation maximization (EM) algorithm is included to estimate the parameters of GGMM-TLE by maximizing the log-likelihood function. Experiments on multiple public datasets demonstrate that GGMM-TLE achieves a superior performance compared with several existing methods in image segmentation tasks.

scholarly journals Genetic Algorithms for Finite Mixture Model Based Voxel Classification in Neuroimaging

2007 ◽  
Vol 26 (5) ◽  
pp. 696-711 ◽  
Author(s):  
J. Tohka ◽  
E. Krestyannikov ◽  
I.D. Dinov ◽  
A.M. Graham ◽  
D.W. Shattuck ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Mixture Model ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Model Based ◽  
Voxel Classification

A finite mixture model for image segmentation

2007 ◽  
Vol 18 (2) ◽  
pp. 137-150 ◽  
Author(s):  
Marco Alfò ◽  
Luciano Nieddu ◽  
Donatella Vicari
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Finite Mixture Model ◽  
Finite Mixture

A finite mixture model for detail-preserving image segmentation

2013 ◽  
Vol 93 (11) ◽  
pp. 3171-3181 ◽  
Author(s):  
Thanh Minh Nguyen ◽  
Q.M. Jonathan Wu ◽  
Dibyendu Mukherjee ◽  
Hui Zhang
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Finite Mixture Model ◽  
Finite Mixture

scholarly journals Color Image Segmentation and Detection of Barrels Using Gaussian Mixture Model

2021 ◽  
Vol 5 (3) ◽  
pp. 52-63
Author(s):  
Susanta Kumar Satpathy ◽  
Devendra Joshi ◽  
G. Ravi Kumar ◽  
Sarita Satpathy
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Image ◽  
Gaussian Mixture ◽  
Color Image Segmentation
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