Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 1476838, 16 pages
http://dx.doi.org/10.1155/2016/1476838
Research Article

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

1Peking University, Beijing 100871, China
2Shenyang University, Shenyang 110044, China
3Northeastern University, Shenyang 110318, China

Received 18 March 2016; Revised 10 July 2016; Accepted 11 July 2016

Academic Editor: Carlos M. Travieso-González

Copyright © 2016 Yang Liu et al. 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. Y. K. Lim and S. U. Lee, “On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques,” Pattern Recognition, vol. 23, no. 9, pp. 935–952, 1990. View at Publisher · View at Google Scholar · View at Scopus
  2. 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
  3. T. Pun, “Entropy threshold: a new approach,” Computer Graphics and Image Processing, vol. 16, no. 3, pp. 210–239, 1981. View at Publisher · View at Google Scholar
  4. N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition, vol. 26, no. 9, pp. 1277–1294, 1993. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Panda, S. Agrawal, and S. Bhuyan, “Edge magnitude based multilevel thresholding using Cuckoo search technique,” Expert Systems with Applications, vol. 40, no. 18, pp. 7617–7628, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Romero and M. Cazorla, “Topological visual mapping in robotics,” Cognitive Processing, vol. 13, supplement 1, pp. S305–S308, 2012. 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. 62–66, 1979. View at Publisher · View at Google Scholar
  8. J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture threshold using the entropy of the histogram,” Computer Vision, Graphics, and Image Processing, vol. 29, no. 3, pp. 273–285, 1985. View at Publisher · View at Google Scholar · View at Scopus
  9. D.-M. Tsai, “A fast thresholding selection procedure for multimodal and unimodal histograms,” Pattern Recognition Letters, vol. 16, no. 6, pp. 653–666, 1995. View at Publisher · View at Google Scholar · View at Scopus
  10. 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
  11. A. Bouaziz, A. Draa, and S. Chikhi, “Artificial bees for multilevel thresholding of iris images,” Swarm and Evolutionary Computation, vol. 21, pp. 32–40, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Gao, S. Kwong, J. Yang, and J. Cao, “Particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi-threshold image segmentation,” Information Sciences, vol. 250, pp. 82–112, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. P. Huang, H. Cao, and S. Luo, “An artificial ant colonies approach to medical image segmentation,” Computer Methods and Programs in Biomedicine, vol. 92, no. 3, pp. 267–273, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. H. V. H. Ayala, F. M. D. Santos, V. C. Mariani, and L. D. S. 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
  15. 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
  16. 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
  17. P. D. Sathya and R. Kayalvizhi, “Modified bacterial foraging algorithm based multilevel thresholding for image segmentation,” Engineering Applications of Artificial Intelligence, vol. 24, no. 4, pp. 595–615, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. C. A. Coello Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256–279, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Bishopp, H. Help, S. El-Showk et al., “A mutually inhibitory interaction between auxin and cytokinin specifies vascular pattern in roots,” Current Biology, vol. 21, no. 11, pp. 917–926, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, vol. 8, no. 1, pp. 687–697, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. G. G. McNickle, C. C. St Clair, and J. F. Cahill Jr., “Focusing the metaphor: plant root foraging behaviour,” Trends in Ecology and Evolution, vol. 24, no. 8, pp. 419–426, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. L. Ma, Y. Zhu, Y. Liu, L. Tian, and H. Chen, “A novel bionic algorithm inspired by plant root foraging behaviors,” Applied Soft Computing Journal, vol. 37, pp. 95–113, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Ma, K. Hu, Y. Zhu, and H. Chen, “A hybrid artificial bee colony optimizer by combining with life-cycle, Powell's search and crossover,” Applied Mathematics and Computation, vol. 252, pp. 133–154, 2015. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  24. L. Ma, K. Hu, Y. Zhu, and H. Chen, “Cooperative artificial bee colony algorithm for multi-objective RFID network planning,” Journal of Network and Computer Applications, vol. 42, pp. 143–162, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Wang, Y. Inukai, and A. Yamauchi, “Root development and nutrient uptake,” Critical Reviews in Plant Sciences, vol. 25, no. 3, pp. 279–301, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. Z. Wang, M. van Kleunen, H. J. During, and M. J. A. Werger, “Root foraging increases performance of the clonal plant Potentilla reptans in heterogeneous nutrient environments,” PLoS ONE, vol. 8, no. 3, Article ID e58602, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Dannowski and A. Block, “Fractal geometry and root system structures of heterogeneous plant communities,” Plant and Soil, vol. 272, no. 1-2, pp. 61–76, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. S. W. Kembel, H. De Kroon, J. F. Cahill Jr., and L. Mommer, “Improving the scale and precision of hypotheses to explain root foraging ability,” Annals of Botany, vol. 101, no. 9, pp. 1295–1301, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proceedings of the Congress on Evolutionary Computation (CEC '02), pp. 1671–1676, IEEE, May 2002.
  30. S. K. Gleeson and J. E. Fry, “Root proliferation and marginal patch value,” Oikos, vol. 79, no. 2, pp. 387–393, 1997. View at Publisher · View at Google Scholar · View at Scopus
  31. C. K. Kelly, “Resource choice in Cuscuta europaea,” Proceedings of the National Academy of Sciences of the United States of America, vol. 89, no. 24, pp. 12194–12197, 1992. View at Publisher · View at Google Scholar · View at Scopus
  32. H. de Kroon, H. Huber, J. F. Stuefer, and J. M. van Groenendael, “A modular concept of phenotypic plasticity in plants,” New Phytologist, vol. 166, no. 1, pp. 73–82, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to B, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, Mich, USA, 1975.
  34. L. Ma, K. Hu, Y. Zhu, B. Niu, H. Chen, and M. He, “Discrete and continuous optimization based on hierarchical artificial bee colony optimizer,” Journal of Applied Mathematics, vol. 2014, Article ID 402616, 20 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. J. Liang, B. Qu, and P. Suganthan, “Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization,” Tech. Rep., Zhengzhou University, 2013. View at Google Scholar
  36. J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. R. C. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” in Proceedings of the 1995 6th International Symposium on Micro Machine and Human Science, pp. 39–43, Nagoya, Japan, October 1995. View at Scopus
  38. D. Corne, M. Dorigo, and F. Glover, New Ideas in Optimization, McGraw-Hill, New York, NY, USA, 1999.
  39. M. A. Potter and K. A. De Jong, “Cooperative coevolution: an architecture for evolving coadapted subcomponents,” Evolutionary computation, vol. 8, no. 1, pp. 1–29, 2000. View at Publisher · View at Google Scholar · View at Scopus
  40. H. Wang and Q. Ni, “A new method of moving asymptotes for large-scale unconstrained optimization,” Applied Mathematics and Computation, vol. 203, no. 1, pp. 62–71, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  41. W. B. Tao, H. Jin, and L. M. Liu, “Object segmentation using ant colony optimization algorithm and fuzzy entropy,” Pattern Recognition Letters, vol. 28, no. 7, pp. 788–796, 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. 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 MathSciNet · View at Scopus
  43. L. Cao, P. Bao, and Z. K. Shi, “The strongest schema learning GA and its application to multilevel thresholding,” Image and Vision Computing, vol. 26, no. 5, pp. 716–724, 2008. View at Publisher · View at Google Scholar · View at Scopus
  44. X. Li, Z. Zhao, and H. D. Cheng, “Fuzzy entropy threshold approach to breast cancer detection,” Information Sciences-applications, vol. 4, no. 1, pp. 49–56, 1995. View at Publisher · View at Google Scholar · View at Scopus
  45. 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
  46. L. Ma and R. C. Staunton, “A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns,” Pattern Recognition, vol. 40, no. 11, pp. 3005–3011, 2007. View at Publisher · View at Google Scholar · View at Scopus
  47. P.-S. Liao, T.-S. Chen, and P.-C. Chung, “A fast algorithm for multilevel thresholding,” Journal of Information Science & Engineering, vol. 17, no. 5, pp. 713–727, 2001. View at Google Scholar · View at Scopus
  48. L. Ma, Y. Zhu, D. Zhang, and B. Niu, “A hybrid approach to artificial bee colony algorithm,” Neural Computing & Applications, vol. 27, no. 2, pp. 387–409, 2016. View at Publisher · View at Google Scholar · View at Scopus