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Truncated Compound Normal with Gamma Mixture Model for Mixture Density Estimation

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
S. Viziananda Row, K. Srinivasa Rao, P. Srinivasa Rao
10.5120/ijca2017912643

Viziananda S Row, Srinivasa K Rao and Srinivasa P Rao. Truncated Compound Normal with Gamma Mixture Model for Mixture Density Estimation. International Journal of Computer Applications 157(3):6-12, January 2017. BibTeX

@article{10.5120/ijca2017912643,
	author = {S. Viziananda Row and K. Srinivasa Rao and P. Srinivasa Rao},
	title = {Truncated Compound Normal with Gamma Mixture Model for Mixture Density Estimation},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {3},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {6-12},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume157/number3/26809-2017912643},
	doi = {10.5120/ijca2017912643},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In this paper, the truncated compound normal with gamma distribution model is formally presented and its density function has been derived for defining a mixture model(TCNGM) based on this as an extension work to the proposed compound normal with gamma mixture(CNGM) model introduced in our earlier work for image segmentation. Update equations for this model have been derived in the context of maximum likelihood estimation(MLE) procedure under Expectation Maximization(EM) framework.

References

  1. Viziananda Row Sanapala, Sreenivasa Rao Kraleti, and Srinivasa Rao Peri. Image Segmentation Using Compound Normal with Gamma Mixture Model, International Journal of Computer Science Issues, Volume 12, Issue 4, July 2015, ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784, www.IJCSI.org.
  2. S. Viziananda Row, Image Segmentation Using Compound Normal with Gamma Mixture Model and its Truncated Version, Ph. D. Thesis, Andhra University, Visakhapatnam, India, 2016.
  3. Normal L. Johnson, Samuel Kotz and N. Balakrishnan. Continuous Univariate Distributions,Vol-I , Second Edition. John Wiley&Sons, 2007.
  4. https://en.wikipedia.org/wiki/Truncated_distribution
  5. Alexander M. Mood, Franklin A. Graybill, and Duane C. Boes. Introduction to the theory of Statistics, Tata McGraw-Hill, Third Edition, 2001.
  6. T. Lei and J. K. Udupa. Performance evaluation of finite normal mixture model-based image segmentaion techniques. IEEE Transactions on Image Processing, vol 12, no 10, pp. 1153-1169, 2003.
  7. J. Zhang and J. M. Modestino. A model fitting approach to cluster validity with application to stochastic model based image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp.1009–1016, 1990.
  8. T. Lei and W. Sewchand. Statistical approach to x-ray CT imaging and its applications in image analysis—Part 2: A new stochastic model based image segmentation technique for CT image, IEEE Transactions on Medical Imaging, vol. 11, no. 1, pp. 62–69, 1992.
  9. Z. Liang and J. R. MacFall. “Parameter estimation and tissue segmentation of multispectral MR images.” IEEE Transactions on Medical Imaging, vol. 13, no.3, pp. 441–449, 1994.
  10. A. P. Dempster, N. M. Laird and D. B. Rubin. Maximum-likelihood from incomplete data via the EM algorithm, J. Royal Statist. Soc. Ser. B (methodological), 39 (1977), pp. 1-38.
  11. J. A. Bilmes. “A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models.” International Computer Science Institute, Berkely CA, 94704, pp 1-13.

Keywords

TCNGM, CNGM, NM, GM, MLE, EM