Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 906861, 14 pages
http://dx.doi.org/10.1155/2014/906861
Research Article

A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching

1School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
2School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
3School of Mathematics and Systems Science & LMIB, Beihang University, Beijing 100191, China

Received 24 December 2013; Accepted 26 March 2014; Published 29 April 2014

Academic Editors: P. Melin, D. Simson, C.-W. Tsai, and F. Yu

Copyright © 2014 Bai Li 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. R. Brunelli and T. Poggio, “Face recognition: features versus templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042–1052, 1993. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, “Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,” IEEE Transactions on Medical Imaging, vol. 20, no. 7, pp. 595–604, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Peng, F. Long, and Z. Chi, “Document image recognition based on template matching of component block projections,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1188–1192, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. G. Fu, H. Zhao, C. Li, and L. Shi, “Road detection from optical remote sensing imagery using circular projection matching and tracking strategy,” Journal of the Indian Society of Remote Sensing, vol. 41, no. 4, pp. 819–831, 2013. View at Publisher · View at Google Scholar
  5. E. Cuevas, A. Echavarría, D. Zaldívar, and M. Pérez-Cisneros, “A novel evolutionary algorithm inspired by the states of matter for template matching,” Expert Systems with Applications, vol. 40, no. 16, pp. 6359–6373, 2013. View at Publisher · View at Google Scholar
  6. H. Grailu, M. Lotfizad, and H. Sadoghi-Yazdi, “An improved pattern matching technique for lossy/lossless compression of binary printed Farsi and Arabic textual images,” International Journal of Intelligent Computing and Cybernetics, vol. 2, no. 1, pp. 120–147, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. J. C. Yoo, H. K. Choi, and C. W. Ahn, “Template matching of occluded object under low PSNR,” Digital Signal Processing, vol. 23, no. 3, pp. 870–878, 2013. View at Publisher · View at Google Scholar
  8. S.-D. Wei and S.-H. Lai, “Fast template matching based on normalized cross correlation with adaptive multilevel winner update,” IEEE Transactions on Image Processing, vol. 17, no. 11, pp. 2227–2235, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. A. C. Berg and J. Malik, “Geometric blur for template matching,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), vol. 1, pp. 607–614, Kauai, Hawaii, USA, December 2001. View at Scopus
  10. A. Rosenfeld and G. J. Vanderbrug, “Coarse-fine template matching,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 7, no. 2, pp. 104–107, 1977. View at Google Scholar · View at Scopus
  11. N. Dong, C.-H. Wu, W.-H. Ip, Z.-Q. Chen, C.-Y. Chan, and K.-L. Yung, “An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection,” Expert Systems with Applications, vol. 38, no. 12, pp. 15172–15182, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. J. W. Zhang and G. G. Wang, “Image matching using a bat algorithm with mutation,” Applied Mechanics and Materials, vol. 203, pp. 88–93, 2012. View at Publisher · View at Google Scholar
  13. F. Liu, H. Duan, and Y. Deng, “A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching,” Optik, vol. 123, no. 21, pp. 1955–1960, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. H. B. Duan, C. F. Xu, S. Liu, and S. Shao, “Template matching using chaotic imperialist competitive algorithm,” Pattern Recognition Letters, vol. 31, no. 13, pp. 1868–1875, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Rudolph, “Convergence rates of evolutionary algorithms for a class of convex objective functions,” Control and Cybernetics, vol. 26, no. 3, pp. 374–390, 1997. View at Google Scholar · View at Scopus
  16. D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Erciyes University Press, Erciyes, Turkey, 2005. View at Google Scholar
  17. C. Ju and C. Xu, “A new collaborative recommendation approach based on users clustering using Artificial Bee Colony algorithm,” The Scientific World Journal, vol. 2013, Article ID 869658, 9 pages, 2013. View at Publisher · View at Google Scholar
  18. H. Sun, H. Luş, and R. Betti, “Identification of structural models using a modified Artificial Bee Colony algorithm,” Computers & Structures, vol. 116, pp. 59–74, 2012. View at Publisher · View at Google Scholar
  19. M. S. Uzer, N. Yilmaz, and O. Inan, “Feature selection method based on Artificial Bee Colony algorithm and support vector machines for medical datasets classification,” The Scientific World Journal, vol. 2013, Article ID 419187, 10 pages, 2013. View at Publisher · View at Google Scholar
  20. H. Sun, H. Waisman, and R. Betti, “Nondestructive identification of multiple flaws using XFEM and a topologically adapting Artificial Bee Colony algorithm,” International Journal for Numerical Methods in Engineering, vol. 95, no. 10, pp. 871–900, 2013. View at Publisher · View at Google Scholar
  21. B. Li, L. G. Gong, and W. L. Yang, “An improved Artificial Bee Colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning,” The Scientific World Journal, vol. 2014, Article ID 232704, 10 pages, 2014. View at Publisher · View at Google Scholar
  22. B. Li, Y. Li, and L. G. Gong, “Protein secondary structure optimization using an improved Artificial Bee Colony algorithm based on AB off-lattice model,” Engineering Applications of Artificial Intelligence, vol. 27, pp. 70–79, 2014. View at Publisher · View at Google Scholar
  23. Z. Yin, X. Liu, and Z. Wu, “A multiuser detector based on Artificial Bee Colony algorithm for DS-UWB systems,” The Scientific World Journal, vol. 2013, Article ID 547656, 8 pages, 2013. View at Publisher · View at Google Scholar
  24. A. Banharnsakun, B. Sirinaovakul, and T. Achalakul, “Job shop scheduling with the best-so-far ABC,” Engineering Applications of Artificial Intelligence, vol. 25, no. 3, pp. 583–593, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. M. K. Apalak, D. Karaboga, and B. Akay, “The Artificial Bee Colony algorithm in layer optimization for the maximum fundamental frequency of symmetrical laminated composite plates,” Engineering Optimization, vol. 46, no. 3, pp. 420–437, 2014. View at Publisher · View at Google Scholar
  26. Q. K. Pan, L. Wang, J. Q. Li, and J. H. Duan, “A novel discrete Artificial Bee Colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation,” Omega, vol. 45, pp. 42–56, 2014. View at Publisher · View at Google Scholar
  27. M. Yahya and M. P. Saka, “Construction site layout planning using multi-objective Artificial Bee Colony algorithm with Levy flights,” Automation in Construction, vol. 38, pp. 14–29, 2014. View at Publisher · View at Google Scholar
  28. S. Zhong, Y. F. Dong, and A. Sarosh, “Artificial Bee Colony algorithm for parametric optimization of spacecraft attitude tracking controller,” in Foundations and Practical Applications of Cognitive Systems and Information Processing, pp. 501–510, Springer, Berlin, Germany, 2014. View at Publisher · View at Google Scholar
  29. S. Biswas, S. Das, S. Debchoudhury, and S. Kundu, “Co-evolving bee colonies by forager migration: a multi-swarm based Artificial Bee Colony algorithm for global search space,” Applied Mathematics and Computation, vol. 232, pp. 216–234, 2014. View at Publisher · View at Google Scholar
  30. J. Zhou, X. Liao, S. Ouyang, R. Zhang, and Y. Zhang, “Multi-objective Artificial Bee Colony algorithm for short-term scheduling of hydrothermal system,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 542–553, 2014. View at Publisher · View at Google Scholar
  31. A. Yurtkuran and E. Emel, “A modified Artificial Bee Colony algorithm for p-center problems,” The Scientific World Journal, vol. 2014, Article ID 824196, 9 pages, 2014. View at Publisher · View at Google Scholar
  32. H. Garg, “Solving structural engineering design optimization problems using an Artificial Bee Colony algorithm,” Journal of Industrial and Management Optimization, vol. 10, no. 3, pp. 777–794, 2014. View at Google Scholar
  33. J. M. García-Torres, S. Damas, O. Cordón, and J. Santamaría, “A case study of innovative population-based algorithms in 3D modeling: Artificial Bee Colony, biogeography-based optimization, harmony search,” Expert Systems with Applications, vol. 41, no. 4, pp. 1750–1762, 2014. View at Publisher · View at Google Scholar
  34. M. K. Ahirwal, A. Kumar, and G. K. Singh, “Adaptive filtering of EEG/ERP through Bounded Range Artificial Bee Colony (BR-ABC) algorithm,” Signal Processing, vol. 25, pp. 164–172, 2014. View at Publisher · View at Google Scholar
  35. J. A. Bullinaria and K. AlYahya, “Artificial Bee Colony training of neural networks,” in Nature Inspired Cooperative Strategies for Optimization (NICSO '13), vol. 512, pp. 191–201, Springer, 2014. View at Publisher · View at Google Scholar
  36. D. Aydin, S. Özyön, C. Yaşar, and T. Liao, “Artificial Bee Colony algorithm with dynamic population size to combined economic and emission dispatch problem,” International Journal of Electrical Power & Energy Systems, vol. 54, pp. 144–153, 2014. View at Publisher · View at Google Scholar
  37. K. Buyukozkan and A. Sarucan, “Applicability of Artificial Bee Colony algorithm for nurse scheduling problems,” International Journal of Computational Intelligence Systems, vol. 7, supplement 1, pp. 121–136, 2014. View at Publisher · View at Google Scholar
  38. B. Li and Y. Yao, “An edge-based optimization method for shape recognition using atomic potential function,” In press.
  39. B. Li, “Research on WNN modeling for gold price forecasting based on improved Artificial Bee Colony algorithm,” Computational Intelligence and Neuroscience, vol. 2014, Article ID 270658, 10 pages, 2014. View at Publisher · View at Google Scholar
  40. B. Li, L. G. Gong, and Y. Yao, “On the performance of internal feedback artificial bee colony algorithm (IF-ABC) for protein secondary structure prediction,” in Proceedings of the 6th International Conference on Advanced Computational Intelligence (ICACI '13), pp. 33–38, Hangzhou, China, 2013. View at Publisher · View at Google Scholar
  41. G. Zhu and S. Kwong, “Gbest-guided artificial bee colony algorithm for numerical function optimization,” Applied Mathematics and Computation, vol. 217, no. 7, pp. 3166–3173, 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. G. Li, P. Niu, and X. Xiao, “Development and investigation of efficient Artificial Bee Colony algorithm for numerical function optimization,” Applied Soft Computing, vol. 12, no. 1, pp. 320–332, 2012. View at Publisher · View at Google Scholar · View at Scopus