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Image Segmentation Algorithm for Images having Asymmetrically Distributed Image Regions

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 21
Year of Publication: 2014
Authors:
P. Chandra Sekhar
K. Srinivasa Rao
P. Srinivasa Rao
10.5120/16922-7076

Chandra P Sekhar, Srinivasa K Rao and Srinivasa P Rao. Article: Image Segmentation Algorithm for Images having Asymmetrically Distributed Image Regions. International Journal of Computer Applications 96(21):64-73, June 2014. Full text available. BibTeX

@article{key:article,
	author = {P. Chandra Sekhar and K. Srinivasa Rao and P. Srinivasa Rao},
	title = {Article: Image Segmentation Algorithm for Images having Asymmetrically Distributed Image Regions},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {21},
	pages = {64-73},
	month = {June},
	note = {Full text available}
}

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.

References

  • Cheng et al (2001) "Color Image Segmentation: Advances and Prospects" Pattern Recognition, Vo1. 34, pp. 2259-2281.
  • Eskicioglu M. A. and Fisher P. S. (1995) "Image Quality Measures and their Performance", IEEE Transactions On Communications, Vol. 43, No. 12, pp. 2959-2965.
  • Gvs Rajkumar, K. Srinivasa Rao, And P. Srinivasa Rao-(2011)-Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and K-Means, "Accepted for Publication" in International Journal of Computer Applications (IJCA) , Vol. 25, No. 4, pp 5-13.
  • Jahne (1995), " A Practical Hand Book on Image segmentation for Scientific Applications,CRC Press.
  • Kelly P. A. et al (1998), "Statistical approach to X-ray CT imaging and its applications in image analysis", IEEE Trans. Med. Imag. ,Vol. 11, No. 1, pp. 53-61.
  • Lei T. et al (2003), "Performance Evaluation of Finite Normal Mixture Model –Based Image Segmentation Techniques", IEEE Transactions On Image Processing, Vol-12, No. 10, pp. 1153-1169.
  • Mantas Paulinas and Andrius Usinskas (2007), "A survey of genentic algorithms applications for image enhancement and segmentation", Information Technology and control, Vol. 36, No. 3, pp. 278-284.
  • M. Seshashayee, K. Srinivasa Rao, Ch. Satyanarayana And P. Srinivasa Rao- (2011) -Image Segmentation Based on a Finite Generalized New Symmetric Mixture Model with K – Means, International journal of Computer Science Issues,Vol. 8, No. 3, pp. 324-331.
  • M. Seshashayee, K. Srinivasa Rao, Ch. Satyanarayana And P. Srinivasa Rao- (2011) –Studies on Image Segmentation method Based on a New Symmetric Mixture Model with K – Means, Global journal of Computer Science and Technology, Vol. 11, No. 18, pp. 51-58.
  • Mclanchan G. and Peel D. (2000)), "The EM Algorithm For Parameter Estimations ", John Wiley and Sons, New York.
  • Nasios N. and Bors A. G. (2006), "Variational learning for Gaussian Mixtures", IEEE Transactions on Systems, Man and Cybernetics, Part B : Cybernetics, Vol. 36(4), pp. 849-862.
  • Pal S. K. and Pal N. R. (1993), "A Review On Image Segmentation Techniques", Pattern Recognition, Vol. 26, No. 9, pp. 1277-1294.
  • P. V. G. D. Prasad Reddy, K. Srinivasa 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, Vol. 7, No. 4. Pp. 317-321.
  • P. V. G. D. Prasad Reddy, K. Srinivasa 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, Vol. 7, No. 4. Pp. 317-321.
  • Srinivas Y. et al (2007), "Unsupervised Image Segmentation based on Finite Doubly Truncated Gaussian Mixture model with K-Means algorithm", International Journal of Physical Sciences, Vol. 19, pp. 107-114.
  • Shital Raut et al (2009), "Image segmentation- A State-Of-Art survey for Prediction", International conference on Adv. Computer control, pp. 420-424.
  • SrinivasYerramalle, K. Srinivasa ao, P. V. G. D. Prasad Reddy-(2010), Unsupervised image segmentation using generalized Gaussian distribution with hierarchical clustering, Journal of advanced research in computer engineering, Vol. 4, No. 1 pp. 43-51.
  • Srinivas Yerramalle And K. Srinivasa Rao-(2007), Unsupervised image classification using finite truncated Gaussian mixture model, Journal of Ultra Science for Physical Sciences, Vol. 19, No. 1, pp 107-114.
  • Rose H. Turi, (2001)," Cluster Based Image Segmentation", Ph. d Thesis, Monash University, Australia.