Skip to main content
SpringerLink
Log in

Artificial Bee Colony Algorithm Applied to Multi-threshold Segmentation

  • Chapter
  • First Online:
Engineering Applications of Soft Computing

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 129))

  • 601 Accesses

Abstract

Image segmentation is a very important task in Computer Vision community, due to its capabilities for further steps that lead to recognizing patterns in digital images. Thus, the process of thresholding selection has become an interesting area, in recent years this procedure has been investigated as an optimization problem. On the other Hand, ABC is a nature inspired algorithm based on the intelligent behaviour of honey-bees which has been successfully used to solve complex real life optimization problems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abak T, Baris U, Sankur B (1997) The performance of thresholding algorithms for optical character recognition. In: Proceedings of international conference on document analytical recognition, pp 697–700

    Google Scholar 

  2. Kamel M, Zhao A (1993) Extraction of binary character/graphics images from grayscale document images, Graph. Models Image Process 55(3):203–217

    Article  Google Scholar 

  3. Trier OD, Jain AK (1995) Goal-directed evaluation of binarization methods. IEEE Trans Pattern Anal Mach Intell 17(12):1191–1201

    Article  Google Scholar 

  4. Bhanu B (1986) Automatic target recognition: state of the art survey. IEEE Trans Aerosp Electron Syst 22:364–379

    Article  Google Scholar 

  5. Sezgin M, Sankur B (2001) Comparison of thresholding methods for non-destructive testing applications, in: IEEE international conference on image processing, pp 764–767

    Google Scholar 

  6. Sezgin M, Tasaltin R (2000) A new dichotomization technique to multilevel thresholding devoted to inspection applications. Pattern Recognit Lett 21(2):151–161

    Article  Google Scholar 

  7. Guo R, Pandit SM (1998) Automatic threshold selection based on histogram modes and discriminant criterion. Mach Vis Appl 10:331–338

    Article  Google Scholar 

  8. Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recognit 26:1277–1294

    Article  Google Scholar 

  9. Shaoo PK, Soltani S, Wong AKC, Chen YC (1988) Survey: a survey of thresholding techniques. Comput Vis Graph Image Process 41:233–260

    Article  Google Scholar 

  10. Snyder W, Bilbro G, Logenthiran A, Rajala S (1990) Optimal thresholding: a new approach. Pattern Recognit Lett 11:803–810

    Article  MATH  Google Scholar 

  11. Chen S, Wang M (2005) Seeking multi-thresholds directly from support vectors for image segmentation. Neurocomputing 67(4):335–344

    Article  Google Scholar 

  12. Chih-Chih L (2006) A novel image segmentation approach based on particle swarm optimization. IEICE Trans Fundam 89(1):324–327

    Google Scholar 

  13. Gonzalez RC, Woods RE (1992) Digital image processing. Addison Wesley, Reading

    Google Scholar 

  14. Gupta L, Sortrakul T (1998) A Gaussian-mixture-based image segmentation algorithm. Pattern Recognit 31(3):315–325

    Article  Google Scholar 

  15. Dempster AP, Laird AP, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(1):1–38

    MathSciNet  MATH  Google Scholar 

  16. Zhang Z, Chen C, Sun J, Chan L (2003) EM algorithms for Gaussian mixtures with split-and-merge operation. Pattern Recognit 36:1973–1983

    Article  MATH  Google Scholar 

  17. Park H, Amari S, Fukumizu K (2000) Adaptive natural gradient learning algorithms for various stochastic models. Neural Netw 13:755–764

    Article  Google Scholar 

  18. Park H, Ozeki T (2009) Singularity and slow convergence of the EM algorithm for Gaussian mixtures. Neural Process Lett 29:45–59

    Article  Google Scholar 

  19. Cuevas E, Zaldivar D, Perez-Cisneros M (2010) Seeking multi-thresholds for image segmentation with Learning Automata. Mach Vis Appl. doi:10.1007/s00138-010-0249-0

    Google Scholar 

  20. Cuevas E, Zaldivar D, Perez-Cisneros M (2010) A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Syst With Appl 37(7):5265–5271

    Article  Google Scholar 

  21. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, technical report-TR06. Erciyes University, Engineering Faculty, Computer Engineering Department

    Google Scholar 

  22. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  23. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    MathSciNet  MATH  Google Scholar 

  24. Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Franklin Inst 346:328–348

    Article  MathSciNet  MATH  Google Scholar 

  25. Pan QK, Tasgetiren MF, Suganthan PN, Chua TJ. A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inform Sci. doi:10.1016/j.ins.2009.12.025

  26. Kang Fei, Li Junjie, Qing Xu (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87:861–870

    Article  Google Scholar 

  27. Zhang Changsheng, Ouyang Dantong, Ning Jiaxu (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37:4761–4767

    Article  Google Scholar 

  28. Karaboga Dervis, Ozturk Celal (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11:652–657

    Article  Google Scholar 

  29. Ho SL, Yang S (2009) An artificial bee colony algorithm for inverse problems. Int J Appl Electromagn Mech 31:181–192

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany

    Margarita-Arimatea Díaz-Cortés & Raúl Rojas

  2. CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Erik Cuevas

Authors
  1. Margarita-Arimatea Díaz-Cortés
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raúl Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Margarita-Arimatea Díaz-Cortés .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Díaz-Cortés, MA., Cuevas, E., Rojas, R. (2017). Artificial Bee Colony Algorithm Applied to Multi-threshold Segmentation. In: Engineering Applications of Soft Computing. Intelligent Systems Reference Library, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-319-57813-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57813-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57812-5

  • Online ISBN: 978-3-319-57813-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation