scholarly journals Performance Evaluation of Image Segmentation Method based on Doubly Truncated Generalized Laplace Mixture Model and Hierarchical Clustering

2017 ◽  
Vol 9 (1) ◽  
pp. 41-49 ◽  
Author(s):  
T Jyothirmayi ◽  
◽  
K.Srinivasa Rao ◽  
P.Srinivasa Rao ◽  
Ch. Satyanarayana
Keyword(s):  
Image Segmentation ◽  
Performance Evaluation ◽  
Mixture Model ◽  
Hierarchical Clustering ◽  
Segmentation Method

Image Segmentation Based on Doubly Truncated Generalized Laplace Mixture Model and K Means Clustering

2016 ◽  
Vol 6 (5) ◽  
pp. 2188
Author(s):  
T Jyothirmayi ◽  
K Srinivasa Rao ◽  
P Srinivasa Rao ◽  
Ch Satyanarayana
Keyword(s):  
Image Segmentation ◽  
Performance Evaluation ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Medical Diagnostics ◽  
Major Drawback ◽  
Practical Applications ◽  
Global Consistency ◽  
The Many ◽  
Security Surveillance

The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and K-Means clustering (DTGLMM-K) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-K algorithm which can be suitable for wide variety of applications and data. Performance evaluation of the developed algorithm has been donethrough various measures like Probabilistic Rand index (PRI), Global Consistency Error (GCE) and Variation of Information (VOI). During the current work case studies forvarious different images having pixel intensities has been carried out and the obtained results indicate the superiority of the developed algorithm for improved image segmentation.

scholarly journals GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 647-650 ◽  
Author(s):  
X. Shi ◽  
Q. H. Zhao
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Posterior Distribution ◽  
Prior Distribution ◽  
Gaussian Mixture ◽  
Fixed Number ◽  
Optimal Number ◽  
Segmentation Method ◽  
Pixel Intensity

For the image segmentation method based on Gaussian Mixture Model (GMM), there are some problems: 1) The number of component was usually a fixed number, i.e., fixed class and 2) GMM is sensitive to image noise. This paper proposed a RS image segmentation method that combining GMM with reversible jump Markov Chain Monte Carlo (RJMCMC). In proposed algorithm, GMM was designed to model the distribution of pixel intensity in RS image. Assume that the number of component was a random variable. Respectively build the prior distribution of each parameter. In order to improve noise resistance, used Gibbs function to model the prior distribution of GMM weight coefficient. According to Bayes' theorem, build posterior distribution. RJMCMC was used to simulate the posterior distribution and estimate its parameters. Finally, an optimal segmentation is obtained on RS image. Experimental results show that the proposed algorithm can converge to the optimal number of class and get an ideal segmentation results.

scholarly journals Studies on Image Segmentation Integrating Generalized Laplace Mixture Model and Hierarchical Clustering

2015 ◽  
Vol 128 (12) ◽  
pp. 7-13
Author(s):  
T. Jyothirmayi ◽  
K. Srinivasa ◽  
P. Srinivasa ◽  
Ch. Satyanarayana
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Hierarchical Clustering

scholarly journals Image Segmentation Based on Doubly Truncated Generalized Laplace Mixture Model and K Means Clustering

2016 ◽  
Vol 6 (5) ◽  
pp. 2188
Author(s):  
T Jyothirmayi ◽  
K Srinivasa Rao ◽  
P Srinivasa Rao ◽  
Ch Satyanarayana
Keyword(s):  
Image Segmentation ◽  
Performance Evaluation ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Medical Diagnostics ◽  
Major Drawback ◽  
Practical Applications ◽  
Global Consistency ◽  
The Many ◽  
Security Surveillance

The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and K-Means clustering (DTGLMM-K) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-K algorithm which can be suitable for wide variety of applications and data. Performance evaluation of the developed algorithm has been donethrough various measures like Probabilistic Rand index (PRI), Global Consistency Error (GCE) and Variation of Information (VOI). During the current work case studies forvarious different images having pixel intensities has been carried out and the obtained results indicate the superiority of the developed algorithm for improved image segmentation.

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