Image Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution

chandoo; K. Srinivasa Rao; P. Srinivasa Rao

Image Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution

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K. Srinivasa Rao

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P. Srinivasa Rao

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GITAM University

Image Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution

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Abstract

Image Segmentation is one of the significant tool for analyzing images, the feature vector of the images are different for different types of images. In remote sensing, Environmental ecological systems, forest studies, conservation of rare animals, the animal images are more important. In this paper we developed and analyze an image segmentation algorithm using mixture of Pearson Type VI Distribution. The Pearsonian Type VI Distribution will characterize the image regions of animal images. The appropriateness Pearsonian Type VI distribution for the pixel intensities of image region in animal images is carried by fitting Pearsonian Type VI Distribution to set of animal images taken from Berkeley image data set. The image segmentation algorithm is developed using EM algorithm for estimating the parameters of the model and maximum likelihood for image component under Bayesian framework. For fast convergence of EM algorithm the initial estimates of the model parameters are obtained by dividing the whole image into K image regions using K-means and Hierarchical clustering algorithm and utilizing the moment method of estimates. The performance of proposed algorithm is studied by conducting an experiment with set of animal images and computing image quality metrics such as PRI, GCE and VOI.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

chandoo, K. Srinivasa Rao, P. Srinivasa Rao. 2014. "Image Segmentation for Animal Images using Finite Mixture of Pearson Type VI Distribution". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F3).

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GJCST Volume 14 Issue F3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

EM algorithm Image Segmentation Performance Measures type VI pearsonian

Classification

I.4.0

Submission ReceivedDecember 10, 2013
Peer Review Double Blind
Handling Editor
Accepted January 2, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

August 21, 2014

Language

English

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Article Matrices

Total Score: 143

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: K. Srinivasa Rao, P. Chandra Sekhar, P. Srinivasa Rao (PhD/Dr. count: 0)

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