Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 3196958, 24 pages
http://dx.doi.org/10.1155/2016/3196958
Research Article

Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image Thresholding Problem

1Department of Electronics and Communication Engineering, Kunming University of Science and Technology, Kunming 650093, China
2Department of Mineral Processing, Kunming University of Science and Technology, Kunming 650093, China

Received 12 May 2016; Revised 22 July 2016; Accepted 27 July 2016

Academic Editor: Masoud Hajarian

Copyright © 2016 Lifang He and Songwei Huang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. S. Linda and C. S. George, Computer Vision, Prentice Hall, Upper Saddle River, NJ, USA, 1st edition, 2001.
  2. L. Barghout and L. Lee, “Perceptual information processing system,” U.S. Patent Application 10/618,543, 2003.
  3. D. L. Pham, C. Xu, and J. L. Prince, “Current methods in medical image segmentation,” Annual Review of Biomedical Engineering, vol. 2, pp. 315–337, 2000. View at Publisher · View at Google Scholar · View at Scopus
  4. J. A. Delmerico, P. David, and J. J. Corso, “Building facade detection, segmentation, and parameter estimation for mobile robot localization and guidance,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '11), pp. 1632–1639, IEEE, San Francisco, Calif, USA, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Yan, H. Zhang, and C. R. Kube, “A multistage adaptive thresholding method,” Pattern Recognition Letters, vol. 26, no. 8, pp. 1183–1191, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Fan, M. Han, and J. Wang, “Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation,” Pattern Recognition, vol. 42, no. 11, pp. 2527–2540, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 962–966, 1979. View at Publisher · View at Google Scholar
  8. W.-H. Tsai, “Moment-preserving thresholding: a new approach,” Computer Vision, Graphics, & Image Processing, vol. 29, no. 3, pp. 377–393, 1985. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986. View at Publisher · View at Google Scholar · View at Scopus
  10. C. H. Li and C. K. Lee, “Minimum cross entropy thresholding,” Pattern Recognition, vol. 26, no. 4, pp. 617–625, 1993. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–168, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Wang, F.-L. Chung, and F. Xiong, “A novel image thresholding method based on Parzen window estimate,” Pattern Recognition, vol. 41, no. 1, pp. 117–129, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. D.-Y. Huang and C.-H. Wang, “Optimal multi-level thresholding using a two-stage Otsu optimization approach,” Pattern Recognition Letters, vol. 30, no. 3, pp. 275–284, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. G. Beni and J. Wang, “Swarm intelligence in cellular robotic systems,” in Robots and Biological Systems: Towards a New Bionics? P. Dario, G. Sandini, and P. Aebischer, Eds., vol. 102 of NATO ASI Series, pp. 703–712, Springer, Berlin, Germany, 1993. View at Publisher · View at Google Scholar
  15. W.-B. Tao, J.-W. Tian, and J. Liu, “Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm,” Pattern Recognition Letters, vol. 24, no. 16, pp. 3069–3078, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Awad, K. Chehdi, and A. Nasri, “Multicomponent image segmentation using a genetic algorithm and artificial neural network,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 4, pp. 571–575, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. P.-Y. Yin, “Multilevel minimum cross entropy threshold selection based on particle swarm optimization,” Applied Mathematics and Computation, vol. 184, no. 2, pp. 503–513, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  18. 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, no. 2, pp. 1341–1350, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. B. Akay, “A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding,” Applied Soft Computing Journal, vol. 13, no. 6, pp. 3066–3091, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. M.-H. Horng, “Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation,” Expert Systems with Applications, vol. 38, no. 11, pp. 13785–13791, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Zhang and L. Wu, “Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach,” Entropy, vol. 13, no. 4, pp. 841–859, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. A. K. Bhandari, A. Kumar, and G. K. Singh, “Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions,” Expert Systems with Applications, vol. 42, no. 3, pp. 1573–1601, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. E. Cuevas, D. Zaldivar, and M. Pérez-Cisneros, “A novel multi-threshold segmentation approach based on differential evolution optimization,” Expert Systems with Applications, vol. 37, no. 7, pp. 5265–5271, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Sarkar and S. Das, “Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy—a differential evolution approach,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4788–4797, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. H. V. H. Ayala, F. M. dos Santos, V. C. Mariani, and L. D. dos Santos Coelho, “Image thresholding segmentation based on a novel beta differential evolution approach,” Expert Systems with Applications, vol. 42, no. 4, pp. 2136–2142, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. M. H. Horng and T. W. Jiang, “Multilevel image thresholding selection based on the firefly algorithm,” in Proceedings of the Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing (UIC/ATC '10), pp. 58–63, Xian, China, October 2010.
  27. M.-H. Horng and R.-J. Liou, “Multilevel minimum cross entropy threshold selection based on the firefly algorithm,” Expert Systems with Applications, vol. 38, no. 12, pp. 14805–14811, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Agrawal, R. Panda, S. Bhuyan, and B. K. Panigrahi, “Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm,” Swarm and Evolutionary Computation, vol. 11, pp. 16–30, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. A. K. Bhandari, V. K. Singh, A. Kumar, and G. K. Singh, “Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy,” Expert Systems with Applications, vol. 41, no. 7, pp. 3538–3560, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, and V. Osuna, “A Multilevel thresholding algorithm using electromagnetism optimization,” Neurocomputing, vol. 139, pp. 357–381, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. K. Hammouche, M. Diaf, and P. Siarry, “A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,” Computer Vision and Image Understanding, vol. 109, no. 2, pp. 163–175, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. S. Manikandan, K. Ramar, M. W. Iruthayarajan, and K. G. Srinivasagan, “Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm,” Measurement, vol. 47, no. 1, pp. 558–568, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. E. Cuevas, F. Sención, D. Zaldivar, M. Pérez-Cisneros, and H. Sossa, “A multi-threshold segmentation approach based on artificial bee colony optimization,” Applied Intelligence, vol. 37, no. 3, pp. 321–336, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. K. Hanbay and M. F. Talu, “Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set,” Applied Soft Computing Journal, vol. 21, pp. 433–443, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Ma, J. Liang, M. Guo, Y. Fan, and Y. Yin, “SAR image segmentation based on artificial bee colony algorithm,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 5205–5214, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. T. Hassanzadeh, H. Vojodi, and A. M. E. Moghadam, “An image segmentation approach based on maximum variance intra-cluster method and firefly algorithm,” in Proceedings of the 7th International Conference on Natural Computation (ICNC '11), pp. 1817–1821, IEEE, Shanghai, China, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. P. Zingaretti, G. Tascini, and L. Regini, “Optimising the color image segmentation,” in VIII Convegno dell Associazione Italiana per l'Intelligenza Artificiale, pp. 1–8, September 2002.
  38. N. S. M. Raja, S. A. Sukanya, and Y. Nikita, “Improved PSO based multi-level thresholding for cancer infected breast thermal images using Otsu,” Procedia Computer Science, vol. 48, pp. 524–529, 2015. View at Publisher · View at Google Scholar
  39. S. Sarkar, S. Das, and S. S. Chaudhuri, “A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution,” Pattern Recognition Letters, vol. 54, pp. 27–35, 2015. View at Publisher · View at Google Scholar · View at Scopus
  40. S. Dey, S. Bhattacharyya, and U. Maulik, “New quantum inspired meta-heuristic techniques for multi-level colour image thresholding,” Applied Soft Computing, vol. 46, pp. 677–702, 2016. View at Publisher · View at Google Scholar
  41. V. Rajinikanth and M. S. Couceiro, “RGB histogram based color image segmentation using Firefly Algorithm,” Procedia Computer Science, vol. 46, pp. 1449–1457, 2015. View at Publisher · View at Google Scholar
  42. T. Kurban, P. Civicioglu, R. Kurban, and E. Besdok, “Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding,” Applied Soft Computing, vol. 23, pp. 128–143, 2014. View at Publisher · View at Google Scholar · View at Scopus
  43. K. N. Krishnanand and D. Ghose, “Detection of multiple source locations using a glowworm metaphor with applications to collective robotics,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '05), pp. 87–94, Pasadena, Calif, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  44. K. N. Krishnanand and D. Ghose, “Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications,” Multiagent and Grid Systems, vol. 2, no. 3, pp. 209–222, 2006. View at Publisher · View at Google Scholar
  45. K. N. Krishnanand and D. Ghose, “Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions,” Swarm Intelligence, vol. 3, no. 2, pp. 87–124, 2009. View at Publisher · View at Google Scholar · View at Scopus
  46. K. N. Krishnanand and D. Ghose, “Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations,” Robotics and Autonomous Systems, vol. 56, no. 7, pp. 549–569, 2008. View at Publisher · View at Google Scholar · View at Scopus
  47. W.-H. Liao, Y. Kao, and Y.-S. Li, “A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks,” Expert Systems with Applications, vol. 38, no. 10, pp. 12180–12188, 2011. View at Publisher · View at Google Scholar · View at Scopus
  48. B. Wu, C. Qian, W. Ni, and S. Fan, “The improvement of glowworm swarm optimization for continuous optimization problems,” Expert Systems with Applications, vol. 39, no. 7, pp. 6335–6342, 2012. View at Publisher · View at Google Scholar · View at Scopus
  49. D. N. Jayakumar and P. Venkatesh, “Glowworm swarm optimization algorithm with topsis for solving multiple objective environmental economic dispatch problem,” Applied Soft Computing, vol. 23, pp. 375–386, 2014. View at Publisher · View at Google Scholar · View at Scopus
  50. L. Qifang, O. Zhe, C. Xin, and Z. Yongquan, “A multilevel threshold image segmentation algorithm based on glowworm swarm optimization,” Journal of Computational Information Systems, vol. 10, no. 4, pp. 1621–1628, 2014. View at Google Scholar
  51. M. H. Horng, “Multilevel image thresholding with glowworm swam optimization algorithm based on the minimum cross entropy,” Advances in Information Sciences and Service Sciences, vol. 5, no. 10, pp. 1290–1298, 2013. View at Google Scholar
  52. Z.-H. Zhan, J. Zhang, Y. Li, and H. S.-H. Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 39, no. 6, pp. 1362–1381, 2009. View at Publisher · View at Google Scholar · View at Scopus
  53. A. K. Qin and P. N. Suganthan, “Self-adaptive differential evolution algorithm for numerical optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05), vol. 2, pp. 1785–1791, IEEE, Edinburgh, Scotland, September 2005. View at Scopus
  54. K. N. Krishnanand and D. Ghose, “Glowworm swarm optimisation: a new method for optimising multi-modal functions,” International Journal of Computational Intelligence Studies, vol. 1, no. 1, pp. 93–119, 2009. View at Publisher · View at Google Scholar
  55. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004. View at Publisher · View at Google Scholar · View at Scopus