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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.

References
  1. Bengt Muthen (1990) “Moments of the censored and truncated bivariate normal distribution”, British Journal of Mathematical and Statistical psychology, No.43, pp.131-143.
  2. Cheng et al (2001) “Color Image Segmentation: Advances and Prospects” Pattern Recognition, Vo1.34, pp. 2259-2281.
  3. Dubes R.C. and Jain A.K. (1989), “Random field models in image analysis”, Journal of Applied Statistics, Vol.16, No.2.
  4. Eskicioglu A.M. and Fisher P.S. (1995) “Image Quality Measures and their Performance”, IEEE Transactions On comm.., Vol.43, No.12, pp.2959-2965.
  5. Feng Zhu et al (2010),” Brain MR image Segmentation based on Gaussian mixture model with spatial information”,3rd International conference on Image and Signal Processing, Vol.3, pp.346-1350.
  6. Jahne (1995), “ A Practical Hand Book on Image segmentation for Scientific Applications, CRC Press.
  7. Juyong Zhang (2010), “A diffusion approach to seeded image segmentation”, Computer Vision and Pattern Recognition, 2010 IEEE Conference, pp. 2125-2132.
  8. Kokkinos I. and Maragos P. (2009),” Synergy between Object Recognition and Image Segmentation Using the Expectation- Maximization Algorithm”, Vol.38, Issue.8, pp. 1486-1501.
  9. Laurent Najman (2011), “On the equivalence between Hierarchical Segmentations and Ultrametric Watersheds”, Vol.40, no.3, pp. 231- 247.
  10. Lie T. et al (1993), “Performance evaluation of Finite normal mixture model based image segmentation, IEEE Transactions on Image processing, Vol .12(10), pp.1153-1169.
  11. Martin D., Fowlkes C., Tal D., and Malik J., (2001) “ A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in proc. 8th Int. Conference Computer vision, vol.2, pp.416- 423.
  12. Mclanchlan G. and Krishnan T. (1997), “The EM Algorithm and Extensions”, John Wiley and Sons, New York -1997.
  13. Mclanchlan G. and Peel D.(2000), “ The EM Algorithm For Parameter Estimations”, John Wileyand Sons, New York -2000.
  14. Meila M. (2005) “Comparing Clustering – An axiomatic view,” in proc.22nd Int. Conf. Machine Learning, pp. 577-584.
  15. Modestino J.W. and Zhang J. (1992), “A Markov Random Field Model Based approach to image Interpretation”, IEEE Trans. Pattern Annl. Machine Intelligence, Vol.14, No.6, Pg.606-615.
  16. Nor Hazlyna et al (2010), “Comparison of acute Leukemia Image Segmentation using HSI and RGB Color spaces”, Information Sciences Signal Processing and their applications, 10th International Conference, pp.749-752.
  17. Norman L. Johnson, Samuel Kortz and Balakrishnan (1994), “Continuous Univariate Distributions” Volume-I, John Wiley and Sons Publications, New York.
  18. Pal S.K. and Pal N.R. (1993), “A Review On Image Segmentation Techniques”, Pattern Recognition,Vol.26, No.9, pp. 1277-1294.
  19. Sangwine S.J. and Horne R.E.N. (1998), “The Colour Image Processing Hand Book,” Chapmann and Hall, UK.
  20. Srinivas Y. and Srinivas Rao K. (2007), “Unsupervised image segmentation using finite doubly truncated Gaussian mixture model and Hierarchial clustering”, Journal of Current Science Vol.93,No.4, pp.507-514.
  21. Srinivas Y. et al (2010),” Unsupervised Image Segmentation Based on Finite Generalized Gaussian Mixture Model With Hierarchical Clustering, International journal for Computational vision and Biomechanics, Vol.3, No.1, pp.73-80.
  22. Tseng, D.C. and Chang C.H. (1992), “Color segmentation usingperceptual attributes,” in Proc.11th IEEE International Conference on Pattern Recognition, Vol.3, pp.228-231.
  23. Udapa J.K et al (1996), “Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation “Graph Models, Image Process, Vol.138, No.3, pp.246-261.
  24. Unnikrishnan R., Pantofaru C., and Hernbert M. (2007), “Toward objective evaluation of image segmentation algorithms,” IEEE Trans.Pattern Annl.Mach.Intell, Vol.29 No.6, pp. 929-944.
  25. Un Tang (2010), “A color image segmentation algorithm based on region growing”, International conference on Computer Engineering and Technology, Vol.6, pp.V6-634.
  26. Yamazaki T. (1998), “Introduction of EM algorithm into color image segmentation,” in Proceedings of ICIRS’98, pp. 368–371. International Journal of Signal Processing 6:1
  27. Ye Hou et al (2009), “Image Segmentation Based on GC- CV”, HIS’ 09. Ninth International conference on Hybrid Intelligent Systems, Vol.1, pp. 252-256.
  28. Zhang Z.H. et al (2003)., “ EM Algorithms for Gaussian Mixtures with Split-and- merge Operation”, Pattern Recognition, Vol. 36(9),pp1973-1983.
  29. Zhenh a Zhang et al (2009), “An improving Technique of Color Histogram in Segmentation-based Image Retrieval “IAS ’09. 5th International Conference, Vol.2, pp. 381-384.
  30. Zhen Wang and Meng Yang (2010), “A fast Clustering algorithm in Image Segmentation”, 2nd International conference on Computer Engineering and Technology, Vol.6, pp. V6-592- V6-594.
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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