CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Bio-Inspired Algorithms for Color Image Segmentation

by Salima Nebti
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 18
Year of Publication: 2013
Authors: Salima Nebti
10.5120/12840-9810

Salima Nebti . Bio-Inspired Algorithms for Color Image Segmentation. International Journal of Computer Applications. 73, 18 ( July 2013), 11-16. DOI=10.5120/12840-9810

@article{ 10.5120/12840-9810,
author = { Salima Nebti },
title = { Bio-Inspired Algorithms for Color Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 18 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number18/12840-9810/ },
doi = { 10.5120/12840-9810 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:40:26.735138+05:30
%A Salima Nebti
%T Bio-Inspired Algorithms for Color Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 18
%P 11-16
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective image segmentation remains a challenging process as it constitutes a critical step to higher level image processing applications such as pattern recognition. In this paper,we present bio-inspired formulationto perform unsupervised image segmentation. Specifically,we used the Quantum PSO, the hybrid Gravitational PSO algorithm, a cooperative gravitational approach and the bees approach as powerful global classifiers to optimize the partition of image data into homogenous regions. The segmentation accuracy based on the bees' algorithm has the highest accuracy.

References
  1. Dinesh D. P, Sonal G. D, Medical Image Segmentation: A Review, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 2, Issue. 1, January 2013, pg. 22 – 27
  2. Rao K. Y, Stephen M. J, Phanindra D. S, Classification Based Image Segmentation Approach, IJCST Vol. 3, Issue 1, ISSN : 2229-4333, 2012.
  3. Chitade A. Z, Katiyar S. K, Colour based image segmentation using K-means clustering, International Journal of Engineering Science and Technology, Vol. 2(10), 2010, 5319-5325. 2010
  4. Dong G, Xie M, Color Clustering and Learning for Image Segmentation Based on Neural Networks, IEEE Transactions on neural networks, Vol. 16, N°. 4, 2005.
  5. Cufi X, Munoz X, Freixenet J, Marti J, A Review on Image Segmentation Techniques Integrating Region and Boundary Information. Advances in Imaging and Electronics Physics, 120:1-32, 2002.
  6. Catalin A. A review on neural network-based image segmentation techniques. 2001.
  7. Moghaddam M. J, Zadeh H. S, Medical Image Segmentation Using Artificial Neural Networks, ISBN: 978-953-307-243-2. 2011.
  8. Ouadfel S, BatoucheM, Ant colony system with local search for Markov random field image segmentation, ICIP (1) 2003: 133-136
  9. Tab, F. A. Naghdy, G. , Mertins, A. Scalable Multiresolution Image Segmentation and Its Application in Video Object Extraction Algorithm, Page(s): 1 – 6, TENCON 2005.
  10. Dey V, Zhang Y, Zhong M, A Review on Image Segmentation Techniques with Remote Seneing Perspective, ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, IAPRS, Vol. XXXVIII, Part 7A, 2010.
  11. Tripathi S, Kumar K , Singh B. K, Singh R. P, Image Segmentation: A Review, International Journal of Computer Science and Management Research, Vol 1 Issue 4, ISSN 2278-733X, 2012.
  12. Zhiwei Ye, Zhengbing Hu, Xudong Lai, Hongwei Chen, Image Segmentation Using Thresholding and Swarm Intelligence, Journal of Software, Vol. 7, NO. 5, 2012.
  13. Chan S. , M. K Tiwari, Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, Book edited by: Felix T. , ISBN 978-3-902613-09-7, pp. 532, Itech Education and Publishing, Vienna, Austria, 2007.
  14. Guofeng J, Wei Z, Zhengwei Y, Zhiyong H, Yuanjia S, Dongdong W, Gan T, Image Segmentation of Thermal Waving Inspection based on Particle Swarm Optimization Fuzzy Clustering Algorithm, Measurement Science Review, Volume 12, No. 6, 2012.
  15. Sa? T. , Çunka? M. "Development of Image Segmentation Techniques using Swarm Intelligence-ABC-Based Clustering Algorithm for Image Segmentation" International Conference on Computing and Information Technology (ICCIT 2012),. pp. 95-100, Saudi Arabia,2012.
  16. Fahd M. A. Mohsen, Mohiy M. Hadhoud, Khalid Amin, A new Optimization-Based Image Segmentation method By Particle Swarm Optimization, 10-18. In IJACSA- International Journal of Advanced Computer Science and Applications, Special Issue on Image Processing and Analysis, 2011.
  17. Jevtic, A. , Quintanilla-Dominguez, J. , Cortina-Januchs, M. G. , and Andina, D. Edge detection using ant colony search algorithm and multiscale contrast enhancement. In Proceedings of the 2009 IEEE International Conference on Systems, Man, & Cybernetics, SMC 2009, pages 2193-2198. 2009.
  18. J. Sun, B. Feng, and W. Xu, (2004) Particle swarm optimization with particles having quantum behavior. In Evolutionary Computation, CEC2004. Congress on. Volume 1, pp 111-116. IEEE.
  19. J. Sun, W. Xu, and B. Feng, (2005) A global search strategy of quantum-behaved particle swarm optimization. In Cybernetics and Intelligent Systems, 2004 IEEE Conference on, volume 1, pp 111–116. IEEE.
  20. J. Kennedy, " The Particle Swarm: Social Adaptation of knowledge", Proceedings of the IEEE International Conference on Evolutionary Computation, Indianapolis, Indiana, USA, pp. 303-308.
  21. Coelho, L. S. (2007) 'Novel Gaussian quantum-behaved particle swarm optimizer applied to electromagnetic design', Science, Measurement & Technology, IET, Vol. 1, No. 5, pp. 290-294. .
  22. Chandragupta Mauryan, K. S. , Thanushkodi K. and Sakthisuganya A. , Reactive Power Optimization Using Quantum Particle Swarm Optimization, Journal of Computer Science 8 (10): 1644-1648, 2012.
  23. Leandro dos Santos Coelho, Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems, Journal of Expert Systems with Applications, Vol 37. Issue 2,Pages: 1676-1683, 2010.
  24. Chatterjee A, Mahanti G. K. , Comparative Performance of Gravitational Search algorithm and modified particle swarm optimization algorithm for synthesis of thinned scanned concentric ring array antenna, Progress In Electromagnetics Research B, Vol. 25, 331-348, 2010.
  25. Gauci M, Dodd T. J, R. Groß, Why 'GSA: a gravitational search algorithm' is not genuinely based on the law of gravity, journal of Natural computing, ISSN 1572 - 9796, Vol. 11, no4, pp. 719-720. 2012.
  26. Rashedi E, Nezamabadi-pour H, Saryazdi S, GSA: a gravitational search algorithm. InfSci 179:2232–2248. 2009.
  27. Ghorbani F. , Nezamabadi-pour H. , On the Convergence Analysis of Gravitational Search Algorithm, Journal of Advances in Computer Research Quarterly ISSN: 2008-6148. Vol. 3, No. 2. Pages: 45-51. 2012.
  28. Mirjalili S. , MohdHashim S. Z. , "A New Hybrid PSOGSA Algorithm for Function Optimization, in IEEE International Conference on Computer and Information Application (ICCIA 2010), China, 2010, pp. 374-377. 2010.
  29. Bahrololoum A. , Nezamabadi-pour H. , Bahrololoum H. , Saeed M. , "A prototype classifier based on gravitational search algorithm", Applied Soft Computing, vol. 12, no. 2, (2012) 819- 825.
  30. M. A. Potter and K. A. D. Jong, "A cooperative coevolutionary approach to function optimization," in PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature. London, UK:Springer-Verlag, 1994, pp. 249–257.
  31. Pham. DT. , Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. :The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK (2005).
  32. Pham DT. , Ghanbarzadeh A. , Koç E. , Otri . S. , Rahim . S. , Zaidi . M. :The Bees Algorithm – A Novel Tool for Complex Optimisation Problems, Proceedings of IPROMS 2006 Conference, pp. 454-461 (2006).
  33. S. Nebti. Color Image Segmentation based on Swarm Optimisation Methods. Lecture Notes in Computer Science, Volume 6377/2010, 277-284, ICICA'2010.
Index Terms

Computer Science
Information Sciences

Keywords

Image segmentation Quantum PSO the Gravitational search algorithm cooperative coevolution the bees algorithm

Powered by PhDFocusTM