Skip to main content
SpringerLink
Log in

Spotted hyena optimizer with lateral inhibition for image matching

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A hybrid spotted hyena optimizer (SHO) based on lateral inhibition (LI) is proposed, it has been applied to solve complication image matching problems. Lateral inhibition mechanism is applied for image pre-process to make intensity gradient in the image contrast enhanced and has the ability to enhance the characters of image, which is able to improve the accuracy of image matching. SHO is inspired from the behavior of social relationship and collaborative of spotted hyenas. This algorithm search for the global optimum mainly through four steps: prey, encircling, attacking prey, and searching prey. In the algorithm, the computation of search location is drastically reduced by incorporating of fitness calculation strategy for solving the real-life optimization problems. The proposed LI-SHO method for image matching mixed together the advantages of SHO and lateral inhibition mechanism. The experiment shows that the proposed algorithm based on lateral inhibition is more effective and feasible in image matching than the other comparing algorithm.

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

Access this article

Log in via an institution

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Amari SI (1977) Dynamics of pattern formation in lateral-inhibition type neural fields. Biol Cybern 27(2):77–87

    Article  MathSciNet  Google Scholar 

  2. Brunelli R (2009) Template matching techniques in computer vision: theory and practice. Wiley, Chichester

    Book  Google Scholar 

  3. Cuevas E, Echavarría A, Zaldívar D et al (2013) A novel evolutionary algorithm inspired by the states of matter for template matching. Expert Syst Appl 40(16):6359–6373

    Article  Google Scholar 

  4. Derrac J, García S, Molina D et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  5. Dhiman G, Kaur A (2017) Spotted Hyena Optimizer for Solving Engineering Design Problems. 2017 International Conference on Machine Learning and Data Science (MLDS), Noida, India

  6. Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70

    Article  Google Scholar 

  7. Dhiman G, Kumar V (2019) Spotted hyena optimizer for solving complex and non-linear constrained engineering problems. In: Yadav N, Yadav A, Bansal J, Deep K, Kim J (eds) Harmony search and nature inspired optimization algorithms. Advances in intelligent systems and computing, vol 741. Springer, Singapore

    Google Scholar 

  8. Duan H, Deng Y, Wang X et al (2013) Small and dim target detection via lateral inhibition filtering and artificial bee Colony based selective visual attention. PLoS One 8(8):e72035

    Article  Google Scholar 

  9. Gibbons J D, Chakraborti S. Nonparametric statistical inference. 3. Ed. revised and expanded. Crc Press, Boca Raton, 2014, 149(3).

    Google Scholar 

  10. Hartline HK (1938) The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. Am J Phys 121(2):400–415

    Article  Google Scholar 

  11. Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley, Hoboken

    MATH  Google Scholar 

  12. Huang L, Duan H, Wang Y (2014) Hybrid bio-inspired lateral inhibition and imperialist competitive algorithm for complicated image matching. Optik 125(1):414–418

    Article  Google Scholar 

  13. Koutaki G, Yata K, Uchimura K, et al (2013) Fast and high accuracy pattern matching using multi-stage refining eigen template. The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, Incheon, South Korea

  14. Li B (2016) An evolutionary approach for image retrieval based on lateral inhibition. Optik 127(13):5430–5438

    Article  Google Scholar 

  15. Li J, Luo Q, Liao L, Zhou Y (2018) Using spotted hyena optimizer for training feedforward neural networks. In: Huang DS, Gromiha M, Han K, Hussain A (eds) Intelligent computing methodologies. ICIC 2018. Lecture notes in computer science, vol 10956. Springer, Cham

    Google Scholar 

  16. Li B, Gong LG, Li Y A novel artificial bee Colony algorithm based on internal-feedback strategy for image template matching. Sci World J 2014(2):906861

    Google Scholar 

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

    Article  Google Scholar 

  18. Malhotra P, Kumar D (2019) An optimized face recognition system using cuckoo search. J Intell Syst. https://doi.org/10.1515/jisys-2017-0127

    Article  Google Scholar 

  19. Naik MK, Panda R (2016) A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl Soft Comput 38:661–675

    Article  Google Scholar 

  20. Orbán G, Horváth G (2013) Algorithm fusion to improve detection of lung cancer on chest radiographs. IJICC 12(1):362–369

    Article  MathSciNet  Google Scholar 

  21. Sun Y, Duan H (2017) Pigeon-inspired optimization and lateral inhibition for image matching of autonomous aerial refueling. Proceedings of the Institution of Mechanical Engineers, Part G Journal of Aerospace Engineering. https://doi.org/10.1177/0954410017696110

    Article  Google Scholar 

  22. Wang X, Duan H, Luo D (2013) Cauchy biogeography-based optimization based on lateral inhibition for image matching. Optik 124(22):5447–5453

    Article  Google Scholar 

  23. Zhang Z, Duan H (2014) A hybrid particle chemical reaction optimization for biological image matching based on lateral inhibition. Optik 125(19):5757–5763

    Article  Google Scholar 

  24. Zhang JW, Wang GG (2012) Image matching using a bat algorithm with mutation. Appl Mech Mater 203(1):88–93

    Article  Google Scholar 

  25. Zhang S, Zhou Y (2017) Template matching using grey wolf optimizer with lateral inhibition. Optik 130:1229–1243

    Article  Google Scholar 

  26. Zhu Y (2003) The research of correlation matching algorithm based on correlation coefficient. Signal Process 19(6):531–534

    Google Scholar 

Download references

Acknowledgments

This work is supported by National Science Foundation of China under Grant No.61563008. Project of Guangxi University for Nationalities Science Foundation under Grant No. 2018GXNSFAA138146.

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Qifang Luo, Jie Li & Yongquan Zhou

  2. Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning, 530006, China

    Qifang Luo & Yongquan Zhou

  3. Department of Computer Science, Jinan University, Guangzhou, 510632, China

    Jie Li

Authors
  1. Qifang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongquan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongquan Zhou.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, Q., Li, J. & Zhou, Y. Spotted hyena optimizer with lateral inhibition for image matching. Multimed Tools Appl 78, 34277–34296 (2019). https://doi.org/10.1007/s11042-019-08081-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08081-3

Keywords

Search

Navigation