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Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and K-Means

by Rajkumar G.V.S., Srinivasa Rao K., Srinivasa Rao P.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 25 - Number 4
Year of Publication: 2011
Authors: Rajkumar G.V.S., Srinivasa Rao K., Srinivasa Rao P.
10.5120/3022-4087

Rajkumar G.V.S., Srinivasa Rao K., Srinivasa Rao P. . Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and K-Means. International Journal of Computer Applications. 25, 4 ( July 2011), 5-13. DOI=10.5120/3022-4087

@article{ 10.5120/3022-4087,
author = { Rajkumar G.V.S., Srinivasa Rao K., Srinivasa Rao P. },
title = { Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and K-Means },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 25 },
number = { 4 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume25/number4/3022-4087/ },
doi = { 10.5120/3022-4087 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:52.209395+05:30
%A Rajkumar G.V.S.
%A Srinivasa Rao K.
%A Srinivasa Rao P.
%T Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and K-Means
%J International Journal of Computer Applications
%@ 0975-8887
%V 25
%N 4
%P 5-13
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A new Image Segmentation method based on Finite Doubly Truncated Bivariate Gaussian Mixture Model is proposed in this paper. The Truncated Bivariate Gaussian Distribution includes several of the skewed and asymmetric distributions as particular cases with finite range. This distribution also includes the Gaussian distribution as a limiting case. We use Expectation maximization (EM) algorithm to estimate the model parameters of the image data and the number of mixture components is estimated by using K-means Clustering algorithm. The K-means clustering algorithm is also utilized for developing the initial estimates of the EM-algorithm. The segmentation is carried out by clustering of feature vector into appropriate component according to the Maximum Likelihood Estimation criteria. The advantage of our method lies its efficiency on initialization of the model parameters and segmenting the images in a totally unsupervised manner. The performance of the proposed algorithm is studied by computing the segmentation performance measures like, PRI, GCE and VOI. The ability of this method for image retrieval is demonstrated by computing the image quality metrics for six images namely OSTRICH, POT, TOWER, BEARS, DEER and BIRD. The experimental results show that this method outperforms the existing model based image segmentation methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Truncated Bivariate Gaussian Mixture Distribution Image Quality Metrics K-means algorithm EM algorithm

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