Skip to main content
SpringerLink
Log in

Modified whale optimization algorithm for underwater image matching in a UUV vision system

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

Abstract

In this paper, a hybrid whale optimization algorithm based on the Lévy flight strategy (LWOA) and lateral inhibition (LI) is proposed to solve the underwater image matching problem in an unmanned underwater vehicle (UUV) vision system. The proposed image matching technique is called the LI-LWOA. The whale optimization algorithm (WOA) simulates encircling prey, bubble-net attacking and searching for prey to obtain the global optimal solution. The algorithm not only can balance the exploration and exploitation but also has high calculation accuracy. The Lévy flight strategy can expand the search space to avoid premature convergence and enhance the global search ability. In addition, the lateral inhibition mechanism is applied to conduct image preprocessing, which enhances the intensity gradient and image characters, and improves the image matching accuracy. The LI-LWOA achieves the complementary advantages of the LWOA and lateral inhibition to improve the image matching accuracy and enhance the robustness. To verify the overall optimization performance of the LI-LWOA, a series of underwater image matching experiments that seek to maximize the fitness value are performed, and the matching results are compared with those of other algorithms. The experimental results show that the LI-LWOA has better fitness, higher matching accuracy and stronger robustness. In addition, the proposed algorithm is a more effective and feasible method for solving the underwater image matching problem.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Abualigah LM (2018) Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

  2. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  3. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Article  Google Scholar 

  4. Barthelemy P, Bertolotti J, Wiersma DS (2008) A levy flight for light. Nature 453(7194):495–498

    Article  Google Scholar 

  5. Bürgmann T, Koppe W, Schmitt M (2019) Matching of TerraSAR-X derived ground control points to optical image patches using deep learning. ISPRS J Photogramm Remote Sens 158:241–248

    Article  Google Scholar 

  6. Chen H, Xue N, Zhang Y, Lu Q, Xia G (2019) Robust visible-infrared image matching by exploiting dominant edge orientations. Pattern Recogn Lett 127:3–10

    Article  Google Scholar 

  7. Cuevas E, Echavarria A, Zaldivar D, Perez-Cisneros M (2013) A novel evolutionary algorithm inspired by the states of matter for template matching. Expert Syst Appl 40(16):6359–6373

    Article  Google Scholar 

  8. 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 

  9. Dou J, Qin Q, Tu Z (2018) Robust image matching based on the information of SIFT. Optik 171:850–861

    Article  Google Scholar 

  10. Duan H, Xu C, Liu S, Shao S (2010) Template matching using chaotic imperialist competitive algorithm. Pattern Recogn Lett 31(13):1868–1875

    Article  Google Scholar 

  11. 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 

  12. Jung HG (2019) K-center algorithm for hierarchical binary template matching. Pattern Recogn Lett 125:584–590

    Article  Google Scholar 

  13. Kennedy J, Eberhart RC (2002) Particle swarm optimization. Int Conf Netw 4:1942–1948

    Google Scholar 

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

    Article  Google Scholar 

  15. Li Y, Jiang Y, Cao J, Wang B, Li Y (2015) AUV docking experiments based on vision positioning using two cameras. Ocean Eng 110:163–173

    Article  Google Scholar 

  16. 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 

  17. Luo Q, Li J, Zhou Y (2019) Spotted hyena optimizer with lateral inhibition for image matching[J]. Multimed Tools Appl 78(24):34277–34296

    Article  Google Scholar 

  18. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Syst 96(96):120–133

    Article  Google Scholar 

  19. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95(95):51–67

    Article  Google Scholar 

  20. Sun H, Du H, Li M, Sun H, Zhang X (2020) Underwater image matching with efficient refractive-geometry estimation for measurement in glass-flume experiments. Measurement 152:107391

    Article  Google Scholar 

  21. 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 

  22. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83

    Article  Google Scholar 

  23. Wu Q, Xu G, Cheng Y, Wang Z, Dong W, Ma L (2019) Robust and efficient multi-source image matching method based on best-buddies similarity measure. Infrared Phys Technol 101:88–95

    Article  Google Scholar 

  24. Xu J, Bi P, Du X, Li J (2019) Robust PCANet on target recognition via the UUV optical vision system. Optik 181:588–597

    Article  Google Scholar 

  25. Xu X, Qi M, Liu H (2019) Real-time stall detection of centrifugal fan based on symmetrized dot pattern analysis and image matching. Measurement 146:437–446

    Article  Google Scholar 

  26. Xu J, Bi P, Du X, Li J, Jiang T (2020) Kernel two-dimensional nonnegative matrix factorization: a new method to target detection for UUV vision system. Complexity, pp 1–13

  27. Yan Z, Jiang L, Zhao Y, Chi D (2012) A novel image matching algorithm application in vision guided AUV docking. Energy Procedia 17:991–1000

    Article  Google Scholar 

  28. Yan Z, Gong P, Zhang W, Li Z, Teng Y (2019) Autonomous underwater vehicle vision guided docking experiments based on L-shaped light Array. IEEE Access 7:72567–72576

    Article  Google Scholar 

  29. Yang X, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  30. Yang W, Fan S, Xu S, King P, Kang BH, Kim E (2019) Autonomous Underwater Vehicle Navigation Using Sonar Image Matching based on Convolutional Neural Network. IFAC-PapersOnLine 52(21):156–162

    Article  MathSciNet  Google Scholar 

  31. Zhang C, Duan H (2015) Biological lateral inhibition and Electimize approach to template matching. Optik 126(7):769–773

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially funded by the National Nature Science Foundation of China under Grant No. 51679057, and partly supported by the Province Science Fund for Distinguished Young Scholars under Grant No. J2016JQ0052.

Author information

Authors and Affiliations

  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Zheping Yan, Jinzhong Zhang & Jialing Tang

Authors
  1. Zheping Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinzhong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jialing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinzhong Zhang.

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

Yan, Z., Zhang, J. & Tang, J. Modified whale optimization algorithm for underwater image matching in a UUV vision system. Multimed Tools Appl 80, 187–213 (2021). https://doi.org/10.1007/s11042-020-09736-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09736-2

Keywords

Search

Navigation