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

21 Cites in Article

Reference Format

1. Cheng (2001). Color Image Segmentation: Advances and Prospects. Pattern Recognition, 1(34), 2259-2281.
2. M Eskicioglu,P Fisher (1995). Image Quality Measures and their Performance. IEEE Transactions On Communications, 43(12), 2959-2965.
3. K Gvs Rajkumar,Srinivasa,And Rao,Srinivasa Rao- (2011). Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and K-Means. International Journal of Computer Applications (IJCA), 25(4), 5-13.
4. Jahne (1995). A Practical Hand Book on Image segmentation for Scientific Applications. A Practical Hand Book on Image segmentation for Scientific Applications
5. P Kelly (1998). Statistical approach to Xray CT imaging and its applications in image analysis. IEEE Trans. Med. Imag, 11(1), 53-61.
6. T Lei (2003). Performance Evaluation of Finite Normal Mixture Model -Based Image Segmentation Techniques. IEEE Transactions On Image Processing, 12(10), 1153-1169.
7. Mantas Paulinas,Andrius Usinskas (2007). A survey of genentic algorithms applications for image enhancement and segmentation. Information Technology and control, 36(3), 278-284.
8. D Marr,E Hildreth (1980). Theory of edge detection. Proceedings of the Royal Society of London. Series B. Biological Sciences, 207(1167), 187-217.
9. M Seshashayee,K Srinivasa,Ch Rao,P Satyanarayana And,Srinivasa Rao- (2011). Image Segmentation Based on a Finite Generalized New Symmetric Mixture Model with K -Means. International journal of Computer Science Issues, 8(3), 324-331.
10. M Seshashayee,K Srinivasa,Ch Rao,P Satyanarayana And,Srinivasa Rao- (2011). Studies on Image Segmentation method Based on a New Symmetric Mixture Model with K -Means. Segmentation for Animal Images using Finite Mixture of Pearson type VI Distribution conference on Adv. Computer control, 11, 420-424.
11. K Kumar,K Rao,Y Srinivas,C Satyanarayana (2010). Studies on Texture Segmentation Using D-Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering. International Journal of Image, Graphics and Signal Processing, 8(3), 45-54.
12. Srinivas Yerramalle,And Rao (2007). Unsupervised image classification using finite truncated Gaussian mixture model. Journal of Ultra Science for Physical Sciences, 19(1), 107-114.
13. H Rose,Turi (2001). Cluster Based Image Segmentation. Cluster Based Image Segmentation
14. G Mclanchan,D Peel (2000). Advanced organic chemistry Third edition), by G. W. Wheland. Pp. xi + 871. John Wiley & Sons Inc., New York; John Wiley & Sons Ltd, London. 1960. E7 net. Endeavour, 20(79), 171.
15. Meila (2005). Comparing clusterings. Proceedings of the 22nd international conference on Machine learning - ICML '05, 577-584.
16. N Nasios,A Bors (2006). Variational learning for Gaussian Mixtures. IEEE Transactions on Systems, Man and Cybernetics, Part B : Cybernetics, 36(4), 849-862.
17. Nikhil Pal,Sankar Pal (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277-1294.
18. P Prasad Reddy,K Rao And,Srinivas Yerramalle,- (2007). supervised image segmentation using finite Generalized Gaussian mixture model with EM & K-Means algorithm. International Journal of Computer Science and Network Security, 7(4), 317-321.
19. P Prasad Reddy,K Rao And,Srinivas Yerramalle,- (2007). supervised image segmentation using finite Generalized Gaussian mixture model with EM & K-Means algorithm. International Journal of Computer Science and Network Security, 7(4), 317-321.
20. Y Srinivas (2007). Unsupervised Image Segmentation based on Finite Doubly Truncated Gaussian Mixture model with K-Means algorithm. International Journal of Physical Sciences, 19, 107-114.
21. Shital Raut,M Raghuwanshi,R Dharaskar,Adarsh Raut (2009). Image Segmentation – A State-Of-Art Survey for Prediction. 2009 International Conference on Advanced Computer Control, 420-424.

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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 August 21, 2014

Version of record

v1.2

Issue date

August 21, 2014

Language

English

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Article Matrices

Total Score: 103

Country: India

Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision

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

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Publish Date: 2014 01, Wed

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