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Image Segmentation Algorithm for Images having Asymmetrically Distributed Image Regions

by P. Chandra Sekhar, K. Srinivasa Rao, P. Srinivasa Rao
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 21
Year of Publication: 2014
Authors: P. Chandra Sekhar, K. Srinivasa Rao, P. Srinivasa Rao
10.5120/16922-7076

P. Chandra Sekhar, K. Srinivasa Rao, P. Srinivasa Rao . Image Segmentation Algorithm for Images having Asymmetrically Distributed Image Regions. International Journal of Computer Applications. 96, 21 ( June 2014), 64-73. DOI=10.5120/16922-7076

@article{ 10.5120/16922-7076,
author = { P. Chandra Sekhar, K. Srinivasa Rao, P. Srinivasa Rao },
title = { Image Segmentation Algorithm for Images having Asymmetrically Distributed Image Regions },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 21 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 64-73 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number21/16922-7076/ },
doi = { 10.5120/16922-7076 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:22:25.286944+05:30
%A P. Chandra Sekhar
%A K. Srinivasa Rao
%A P. Srinivasa Rao
%T Image Segmentation Algorithm for Images having Asymmetrically Distributed Image Regions
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 21
%P 64-73
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation is one of the most important prerequisite for image analysis. This paper addresses the problem of model based image segmentation using mixture of Pearsonian Type I Distribution. Here the whole image is characterized by a mixture of K-components Type I Personian Distribution. The Pearsonian Type I Distribution is capable of portraying the asymmetric nature of image regions more close to the reality. The model parameters estimated by EM Algorithm. The initialization of model parameters is done through the integrating the histogram method, K-means algorithm and moment of method of estimators. The Image Segmentation algorithm is developed using component maximum likelihood. The proposed algorithm is evolved by conducting experiments with 5 images taken from Berkeley image data set. The Experiments revealed that this algorithm performs better than that of Gaussian mixture model with respect to image segmentation quality measures such as PRI, VOC and GCE for some images taken in sky and on earth.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Type I Pearsonian distribution EM algorithm K-means algorithm.

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