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Enhanced Color Image Segmentation of Foreground Region using Particle Swarm Optimization

by Manas Yetirajam, Pradeep Kumar Jena
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 8
Year of Publication: 2012
Authors: Manas Yetirajam, Pradeep Kumar Jena
10.5120/9134-3322

Manas Yetirajam, Pradeep Kumar Jena . Enhanced Color Image Segmentation of Foreground Region using Particle Swarm Optimization. International Journal of Computer Applications. 57, 8 ( November 2012), 18-23. DOI=10.5120/9134-3322

@article{ 10.5120/9134-3322,
author = { Manas Yetirajam, Pradeep Kumar Jena },
title = { Enhanced Color Image Segmentation of Foreground Region using Particle Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 8 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number8/9134-3322/ },
doi = { 10.5120/9134-3322 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:54.246874+05:30
%A Manas Yetirajam
%A Pradeep Kumar Jena
%T Enhanced Color Image Segmentation of Foreground Region using Particle Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 8
%P 18-23
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a new segmentation approach which aims to segment only the foreground of an image after background elimination. Background elimination is treated as an optimization problem and is solved by using principle of PSO. The proposed algorithm is a thresholding method used to eliminate background from an image assuming that the image to be threshold contains two classes of pixels or bi-modal histogram(foreground and background). This gives a low level binary representation to the image eliminating the background and highlighting the foreground part. Based on the distance and similarity among the connected components in the binary image, it is segmented and a different similar color is assigned to each of the segment to preserve the color information contained in the real color image.

References
  1. Linda G. Shapiro and George C. Stockman "Computer Vision", pp 279-325, New Jersey, Prentice-Hall.
  2. R. C. Gonzalez, R. E. Woods, "Digital Image Processing", Pearson Education, 2001.
  3. B. Chanda, D. Dutta Majumder, "Digital Image Processing and Analysis" Prentice Hall India Publication, 2011.
  4. S. Sridhar "Digital Image Processing",Oxford.
  5. H. A. R. Akkar, "Optimization of Artificial Neural Networks by Using Swarm Intelligent".
  6. F. M. A. Mohsen, M. M. Hadhoud, K. Amin, "A new Optimization-Based Image Segmentation method By Particle Swarm Optimization", (IJACSA) International Journal of Advanced Computer Science and Applications, Special Issue on Image Processing and Analysis.
  7. C. Blum , D. Merkle , "Swarm Intelligence" , Springer-Verlag Berlin Heidelberg, 2008.
  8. J. Han, M. Kamber, J. pei "Data Mining Concepts and Techniques", ELSEVIER.
  9. A. A. Younes, I. Truck, and H. Akdaj, "Color Image Profiling Using Fuzzy Sets," Turk J Elec. Engin. , Vol. 13, No. 3, 2005.
  10. P. Yin, "Multilevel minimum cross entropy threshold selection based on particle swarm optimization," Applied Mathematics and Computation, Vol. 184, pp. 503–513, 2007.
  11. T. Hongmei, W. Cuixia, H. Liying, and W. Xia, "Image Segmentation Based on Improved PSO," the proceedings of the International Conference on Computer and Communication Technologies in Agriculture Engineering(CCTAE2010), pp. 191-194, 2010.
  12. C. Wei and F. Kangling, "Multilevel Thresholding Algorithm Based on Particle Swarm Optimization for Image Segmentation," in the Proceedings of the 27th Chinese Control Conference, July 16-18, Kunming, Yunnan, China, pp. 348-351, 2008.
  13. M. Maitra and A. Chatterjee, "A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding," Expert Systems with Applications, Vol. 34, pp. 1341– 1350, 2008.
  14. Dr. B. P. Mallikarjunaswamy , Karunakara K "Graph Based Approach for Background Elimination and Segmentation of the Image" Research Journal of Computer Systems Engineering- An International Journal, Vol 02, Issue 02, June, 2011.
  15. T. Dash, T. Nayak, S. Chattopadhyay "Offline Handwritten Signature Verification using Associative Memory Net". International Journal of Advanced Research in Computer Engineering & Technology (2012): 1(4): 370-374.
  16. T. Dash, S. Chattopadhyay, T. Nayak "Handwritten Signature Verification using Adaptive Resonance Theory Type-2 (ART-2) Net". Journal of Global Research in Computer Science (2012) 3(8): 21-25.
  17. Panda S. , Sahoo S. , Jena P. K. , Chattopadhyay S. "Comparing Fuzzy-C means and K-means Clustering Techniques: a Comprehensive Study". In Proceedings of 2nd International Conference on Computer Science, Engineering & Applications, by D. C. Wyld, J. Zizka, D. Nagamalai (Eds. ), Advances in Intelligent and Soft Computing (AISC) Vol. 166, pp. 451-460. , New Delhi India.
  18. Manas Yetirajam, Manas Ranjan Nayak, Subhagata Chattopadhyay, "Recognition and Classification of Broken Characters using Feed Forward Neural Network to Enhance an OCR Solution" International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 1, Issue 8, October 2012.
  19. S. K. Sahu,S. Sahani,P. K. Jena,S. Chattopadhyay, " Fingerprint Identification System using Tree Based Matching", International Journal of Computer Application, Vol 53 No. 10 Sept 2012
  20. M. A. Herraez, J Domingo and F. J. Ferri,"Combining similarity measures in content-based image retrieval," ELSEVIER,2008.
  21. J. Xu, B. Xu and S. Men, "Feature-based Similarity Retrieval in Content-based Image Retrieval," in the proceeding of Seventh Web Information Systems and Applications Conference, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Thresholding Background Elimination Segmentation Feature Extraction Particle Swarm Optimization

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