Skip to main content

Advertisement

Springer Nature Link
Log in

A template matching approach based on the behavior of swarms of locust

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

For many image processing applications (such as feature tracking, object recognition, stereo matching and remote sensing), the technique known as Template Matching (TM) plays an important role for the localization and recognition of objects or patterns within a digital image. A TM approach seeks to find a position within a source image which yields to the best possible resemblance between a given sub-image (typically referred as image template) and a corresponding region of such source image. TM involves two critical aspects: similarity measurement and search strategy. In this sense, the simplest available TM method involves an exhaustive computation of the Normalized Cross-Correlation (NCC) value (similarity measurement) over all pixel locations of the source image (search strategy). Unfortunately, this approach is strongly restricted due to the high computational cost implied in the evaluation of the NCC coefficient. Recently, several TM methods based on evolutionary approaches have been proposed as an alternative to reduce the number of search locations in the TM process. However, the lack of balance between exploration and exploitation related to the operators employed by many of such approaches makes TM to suffer from several critical flaws, such as premature convergence. In the proposed approach, the swarm optimization method known as Locust Search (LS) is applied to solve the problem of template matching. The unique evolutionary operators employed by LS method’s search strategy allows to explicitly avoid the concentration of search agents toward the best-known solutions, which in turn allows a better exploration of the valid image’s search region. Experimental results show that, in comparison to other similar methods, the proposed approach achieves the best balance between estimation accuracy and computational cost.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Brunelli R (2009) Template matching techniques in computer vision: theory and practice. Wiley. ISBN: 978-0-470-51706-2

  2. Hadi G, Mojtaba L, Hadi SY (2009) An improved pattern matching technique for lossy/lossless compression of binary printed Farsi and Arabic textual images. Int J Intell Comput Cybern 2(1):120–147

    Article  MathSciNet  MATH  Google Scholar 

  3. Krattenthaler W, Mayer KJ, Zeiler M (1994) Point correlation: a reduced-cost template matching technique. In: Proceedings of the first IEEE international conference on image processing, pp 208–212

  4. Rosenfeld A, VanderBrug GJ (1977) Coarse-fine template matching. IEEE Trans Syst, Man, Cybern SMC-7(2):104–107

    MATH  Google Scholar 

  5. Tanimoto SL (1981) Template matching in pyramids. Comput Vis Graph, Image Process 16(4):356–369

    Article  Google Scholar 

  6. Dong N, Wu C-H, Ip W-H, Chen Z-Q, Chan C-Y, Yung K-L et al (2011) An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection. Expert Syst Appl 38:15172–15182. Liu, F

    Article  Google Scholar 

  7. Mitchell M (1996) An introduction to Genetic Algorithms, Cambridge, MA

  8. Kennedy J, Eberhart R (1995) Particle Swarm Optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, 4, pp 1942–1248

  9. Haibin D, Chunfang X, Senqi L, Shan S (2010) Template matching using chaotic imperialist competitive algorithm. Pattern Recogn Lett 31:1868–1875

    Article  Google Scholar 

  10. Chen G, Low CP, Yang Z (2009) Preserving and exploiting genetic diversity in evolutionary programming algorithms. IEEE Trans Evol Comput 13(3):661–673

    Article  Google Scholar 

  11. Adra SF, Fleming PJ (2011) Diversity management in evolutionary many objective optimization. IEEE Trans Evol Comput 15(2):183–195

    Article  Google Scholar 

  12. Tan KC, Chiam SC, Mamun AA, Goh CK (2009) Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Eur J Oper Res 197:701–713

    Article  MATH  Google Scholar 

  13. Cuevas Erik, González A, Zaldívar D, Pérez-Cisneros M (2015) An optimisation algorithm based on the behaviour of locust swarms. Int J Bio-Inspired Comput 7:6:402–407

    Article  Google Scholar 

  14. Yazdi HS (2009) An improved pattern matching technique for lossy/lossless compression of binary printed farsi and arabic textual images. International Journal of Intelligent Computing and Cybernetics, pp 120–147

  15. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. Engine, Engineering Faculty, Computer Engineering Department. Erciyes University

  16. Hossein A, Hossein-Alavi A (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 17:4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  17. Cuevas E, Echavarría A, Zaldívar D, Pérez-Cisneros M (2013) A novel evolutionary algorithm inspired by the states of matter for template matching. Expert Systx Appl 40:6359

    Article  Google Scholar 

  18. Topaz CM, Bernoff AJ, Logan S, Toolson W (2008) A model for rolling swarms of locusts. Eur Phys J Spec Top 157:93–109

    Article  Google Scholar 

  19. Topaz CM, D’Orsogna Maria R, Edelstein-Keshet L, Bernoff Andrew J Locust dynamics: behavioral phase change and swarming. Plos Comput Biol 8(8):1–11

  20. Cuevas E, González A, Fausto F, Zaldívar D, Pérez-Cisneros M (2015) Multithreshold segmentation by using an algorithm based on the behavior of locust swarms, Mathematical Problems in Engineering, vol 2015, Article ID 805357, 25 p, doi:10.1155/2015/805357

  21. Yazdi HS (January 2009) An improved pattern matching technique for lossy/lossless compression of binary printed farsi and arabic textual images. International Journal of Intelligent Computing and Cybernetics, pp 120–147

  22. Dong N, Wu C-H, Ip W-H, Chen Z-Q, Chan C-Y, Yung K-L (2011) An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection. Expert Syst Appl 38(12):15172–15182

    Article  Google Scholar 

  23. Liu F, Duan H, Deng Y (2012) A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching. Optik - Int J Light Electron Opt 123(21):1955–1960

    Article  Google Scholar 

  24. Wu C-H, Wang D-Z, Ip A, Wang D-W, Chan C-Y, Wang H-F (2009) A particle swarm optimization approach for components placement inspection on printed circuit boards. J Intell Manuf 20(5):535549

    Article  Google Scholar 

  25. Chen G, Low CP, Yang Z (2009) Preserving and exploiting genetic diversity in evolutionary programming algorithms. IEEE Trans Evol Comput 13(3):661673

    Article  Google Scholar 

  26. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83

    Article  MathSciNet  Google Scholar 

  27. Garcia S, Molina D, Lozano M, Herrera F (2008) A study on the use of nonparametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special session on real parameter optimization. Journal of Heuristics

  28. Oliva D, Cuevas E, Pajares G et al (2014) Appl Intell 41:791. doi:10.1007/s-10489-014-0552-y

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad de Guadalajara, Blvd. Marcelino García Barragán #1421, C.P. 44430, Guadalajara, Jalisco, México

    Adrián González, Erik Cuevas, Fernando Fausto & Arturo Valdivia

  2. Department of Computer Science, Freie Universität Berlin, Arnimallee 7, 14195, Berlin, Germany

    Raúl Rojas

Authors
  1. Adrián González
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Erik Cuevas
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fernando Fausto
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Arturo Valdivia
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Raúl Rojas
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Erik Cuevas.

Appendix

Appendix

Images dataset used on our experimental setup. (a) Waldo 1, (b) Circuit board 1, (c) Satellite image, (d) Waldo 2, (e) Circuit board 2, and (f) Circuit board 3.

Table 4
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

González, A., Cuevas, E., Fausto, F. et al. A template matching approach based on the behavior of swarms of locust. Appl Intell 47, 1087–1098 (2017). https://doi.org/10.1007/s10489-017-0937-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-017-0937-9

Keywords