Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Using Ant Colony Optimization and Self-organizing Map for Image Segmentation

  • Conference paper
MICAI 2007: Advances in Artificial Intelligence (MICAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4827))

Included in the following conference series:

Abstract

In this study, ant colony optimization (ACO) is integrated with the self-organizing map (SOM) for image segmentation. A comparative study with the combination of ACO and Simple Competitive Learning (SCL) is provided. ACO follows a learning mechanism through pheromone updates. In addition, pheromone and heuristic information are normalized and the effects on the results are investigated in this report. Preliminary experimental results indicate that the normalization of the parameters can improve the image segmentation results.

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 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)

    Google Scholar 

  2. Pham, D.T., Karaboga, D.: Intelligent Optimization Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. Springer, Heidelberg (2000)

    Google Scholar 

  3. Hung, C.C., Scheunders, P.M., Pham, M.-C.S., Coleman, T.: Using Intelligent Optimization Techniques in the K-means Algorithm for Multispectral Image Classification. Int. J. Fuzzy Syst. 6(3), 105–115 (2004)

    MathSciNet  Google Scholar 

  4. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cyb. Part B 26, 29–41 (1996)

    Article  Google Scholar 

  5. Yuqing, P., Xiangdan, H., Shang, L.: The K-means clustering Algorithm Based on Density and Ant Colony. In: International Conference on Neural Networks and Signal Processing, Nanjing, China, vol. 1, pp. 457–460 (2003)

    Google Scholar 

  6. Monmarch´e, N.: On data clustering with artificial ants. In: Freitas, A.A. (ed.) AAAI-1999 and GECCO-1999 Workshop on Data Mining with Evolutionary Algorithms: Research Directions, Orlando, Florida, pp. 23–26 (1999)

    Google Scholar 

  7. Monmarch´e, N., Slimane, M., Venturini, G.: AntClass: discovery of clusters in numeric data by an hybridization of ant colony with k-means algorithm, Internal Report no. 213 Laboratoire d’Informatique, E3i, Universite de Tours, Tours, France (1999)

    Google Scholar 

  8. Bin, W., Yi, Z., Shaohui, L., Zhongzhi, S.: CSIM: A Document Clustering Algorithm Based on Swarm Intelligence. In: Congress on Evolutionary Computation, Honolulu, HI, vol. 1, pp. 477–482 (2002)

    Google Scholar 

  9. Kanade, P.M., Hall, L.O.: Fuzzy Ants as a Clustering Concept. In: Proceedings of 22nd International Conference of the North American Fuzzy Information Processing Society, Chicago, IL, pp. 227–232 (2003)

    Google Scholar 

  10. Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  11. Vesanto, J., Alhoniemi, E.: Clustering of the Self-Organizing Map. IEEE transaction on Neural Networks 11(3) (2000)

    Google Scholar 

  12. Rumelhart, D.E., Zipser, D.: Feature discovery by competitive learning. In: McClelland, J.L., Rumelhart, D.E. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 151–193. MIT Press, Cambridge (1986)

    Google Scholar 

  13. Saatchi, S., Hung, C.C., Kuo, B.C.: A comparison of the improvement of k-means and simple competitive learning algorithms using ant colony optimization. In: 7th International Conference on Intelligent Technology, Taipei, Taiwan (2006)

    Google Scholar 

  14. Saatchi, S., Hung, C.C.: Hybridization of the Ant Colony Optimization with the K-means Algorithm for Clustering. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 511–520. Springer, Heidelberg (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing and Software Engineering, Southern Polytechnic State University, 1100 South Marietta Parkway, Marietta, GA 30060, USA

    Sara Saatchi & Chih-Cheng Hung

Authors
  1. Sara Saatchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chih-Cheng Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Alexander Gelbukh Ángel Fernando Kuri Morales

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Saatchi, S., Hung, CC. (2007). Using Ant Colony Optimization and Self-organizing Map for Image Segmentation. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76631-5_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76630-8

  • Online ISBN: 978-3-540-76631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation