Skip to main content

Advertisement

SpringerLink
Log in

RETRACTED ARTICLE: Enhanced artificial bee Colony algorithm and its application in multi-threshold image feature retrieval

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

This article was retracted on 20 September 2022

This article has been updated

Abstract

In human information, visual information accounts for about 60%. Image is the main way for humans to obtain visual information. Image binarization is an important technique in image pre-processing. It has important applications in pattern recognition, optical character recognition, and medical imaging. This paper first introduces the research background, basic principle, elements, algorithm flow, advantages, and disadvantages of bee colony algorithm. To solve the problem that artificial bee colony algorithm is easy to fall into local optimum, this paper proposes an adaptive cauchy mutation artificial bee colony algorithm. This algorithm introduces adaptive factors to expand the search range of bee colony and uses the characteristics of Cauchy distribution to search the global, which improves the universality of bee colony search. Then using stochastic process theory, the adaptive Cauchy mutation artificial bee colony algorithm is analysed theoretically, and the convergence of the algorithm has demonstrated. Finally, we can use Matlab to implement the artificial bee colony algorithm, and we can optimize the Griewank function and Sphere function. In addition, this paper has successfully used in image multi-threshold feature retrieval. Compared with the conventional methods, it shows that the method is more accurate.

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

Similar content being viewed by others

Change history

References

  1. Akay B, Karaboga D (2015) A survey on the applications of artificial bee colony in signal, image, and video processing. Signal, Image & Video Processing 9(4):967–990

    Article  Google Scholar 

  2. Akbar H, Suryana N, Sahib S (2015) Chaotic clonal selection optimisation for multi-threshold segmentation. International Journal of Signal and Imaging Systems Engineering 8(5):298–315

    Article  Google Scholar 

  3. Aparna R (2017) Swarm intelligence for automatic video image contrast adjustment. International Journal of Rough Sets & Data Analysis 3(3):21–37

    Article  Google Scholar 

  4. Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41(7):3538–3560

    Article  Google Scholar 

  5. Chakraborty S, Chatterjee S, Dey N, Ashour AS, Ashour AS, Shi F, Mali K (2017) Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc Res Tech 80(10):1051–1072

    Article  Google Scholar 

  6. Huang, Y., Liu, Z., & Shi Y. (2015). Quantitative analysis of live lymphocytes morphology and intracellular motion in microscopic images. Biomedical Signal Processing & Control, 18(4), 195–203

  7. Jiang Y, Tsai P, Yeh WC, Cao L (2016) A honey–bee-mating based algorithm for multilevel image segmentation using Bayesian theorem. Appl Soft Comput 52(7):1181–1190

    Google Scholar 

  8. Kaplan G, Avdan U (2017) Object-based water body extraction model using Sentinel-2 satellite imagery. European Journal of Remote Sensing 50(1):137–143

    Article  Google Scholar 

  9. Kumar S, Pant M, Kumar M, Dutt A (2018) Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms. International Journal of Machine Learning & Cybernetics 9(1):163–183

    Article  Google Scholar 

  10. Ljouad T, Amine A, Rziza M (2014) A hybrid mobile object tracker based on the modified cuckoo Ssearch algorithm and the Kalman filter. Pattern Recogn 47(11):3597–3613

    Article  Google Scholar 

  11. Luo Q, Yang Z, Chen X, Zhou Y (2014) A multilevel threshold image segmentation algorithm based on glowworm swarm optimization. Journal of Computational Information Systems 10(4):1621–1628

    Google Scholar 

  12. Ma Y (2017) Application of edge detection and image segmentation algorithm of image processing in murals copy. Revista de la Facultad de Ingeniería 32(5):809–808

    Google Scholar 

  13. Norouzi A, Rahim MSM, Altameem A, Saba T, Rad A (2014) Medical image segmentation methods, algorithms, and applications. IETE Tech Rev 31(3):199–213

    Article  Google Scholar 

  14. Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst Appl 79(1):164–180

    Article  Google Scholar 

  15. Pankratova ND (2014) System coordination of survivability and safety of complex engineering objects operation. Computer Science Journal of Moldova 22(3):303–317

    MATH  Google Scholar 

  16. Pant S, Kumar A, Ram M (2017) Flower pollination algorithm development: a state of art review. International Journal of System Assurance Engineering & Management 8(2):1858–1866

    Google Scholar 

  17. Ranjani JJ (2014) Bi-level thresholding for binarisation of handwritten and printed documents. Computer Vision Let 9(1):41–50

    Google Scholar 

  18. Salgotra R, Singh U (2017) Application of mutation operators to flower pollination algorithm. Expert Syst Appl 79(15):112–119

    Article  Google Scholar 

  19. Saraswat M, Arya KV (2014) Automated microscopic image analysis for leukocytes identification: a survey. Micron 65(5):20–33

    Article  Google Scholar 

  20. Sudhakar B, Reddy AS (2014) Hybrid FCM with watershed algorithm for image segmentation. International Journal of Engineering Trends & Technology 18(6):264–268

    Article  Google Scholar 

  21. Vashistha S, Gupta ES (2015) A review on various approaches utilized for image segmentation. International Journal of Engineering and Computer Science 4(6):12327–12332

    Google Scholar 

  22. Yao B, Yan Q, Zhang M, Yang Y (2017) Improved artificial bee colony algorithm for vehicle routing problem with time windows. PLoS One 12(9):1–18

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers and the editor for suggesting various changes. And this work was supported by the National Natural Science Foundation of China (No. 81473559), the Science Basic Research Program in Shaanxi Province of China (No. 16JK1823), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2017JM6086), the Science Basic Research Program in Xianyang Normal University of China (No. XSYK17030), the Education Scientific Program of 13th Five-year Plan in Shaanxi Province of China (no. SGH17H196), the Teaching Reform Program in Xianyang Normal University of China (No. 2017Z014).

Author information

Authors and Affiliations

  1. School of Computer Science, XianYang Normal University, XianYang, Shaanxi, China

    Hong Li & Weibin Li

Authors
  1. Hong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weibin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Li.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Li, W. RETRACTED ARTICLE: Enhanced artificial bee Colony algorithm and its application in multi-threshold image feature retrieval. Multimed Tools Appl 78, 8683–8698 (2019). https://doi.org/10.1007/s11042-018-6066-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6066-6

Keywords

Search

Navigation