Aggregation Pheromone Density Based Image Segmentation

  • Susmita Ghosh
  • Megha Kothari
  • Ashish Ghosh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity is called aggregation pheromone. This paper presents a novel method for image segmentation considering the aggregation behavior of ants. Image segmentation is viewed as a clustering problem which aims to partition a given set of pixels into a number of homogenous clusters/segments. At each location of data point representing a pixel an ant is placed; and the ants are allowed to move in the search space to find out the points with higher pheromone density. The movement of an ant is governed by the amount of pheromone deposited at different points of the search space. More the deposited pheromone, more is the aggregation of ants. This leads to the formation of homogenous groups of data. The proposed algorithm is evaluated on a number of images using different cluster validity measures. Results are compared with those obtained using average linkage and k-means clustering algorithms and are found to be better.


Image Segmentation Average Linkage Aggregation Pheromone Remote Sensing Image Image Segmentation Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson education, London (2003)Google Scholar
  2. 2.
    Pal, N., Pal, S.: A review on image segmentation techniques. Pattern Recognition 26(9), 1277–1294 (1993)CrossRefGoogle Scholar
  3. 3.
    Kettaf, F.Z., Bi, D., de Beauville, J.P.A.: A comparision study of image segmentation by clustering techniques. In: Proceedings of ICSP 1996, pp. 1280–1283 (1996)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Chanda, B., Majumder, D.D.: Digital Image Processing and Image Analysis. Prentice Hall of India, New Delhi (2003)Google Scholar
  6. 6.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting: Robot-like ants and ant-like robots. In: Meyer, J.A., Wilson, S.W. (eds.) Proceedings of the 1st Conference on Simulation of Adaptive Behavior: From Animals to Animats, vol. 1, pp. 356–365. MIT press/Bradford Books (1991)Google Scholar
  7. 7.
    Lumer, E.D., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Cliff, D., Husbands, P., Meyer, J.A., Wilson, S.W. (eds.) Proceedings of the 3rd International Conference on Simulation of Adaptive Behaviour:From Animals to Animats, vol. 3, pp. 501–508 (1994)Google Scholar
  8. 8.
    Monmarché, N., Slimane, M., Venturini, G.: On improving clustering in numerical database with artificial ants. In: Floreano, D., Mondada, F. (eds.) ECAL 1999. LNCS, vol. 1674, pp. 626–635. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  9. 9.
    Handl, J., Knowles, J., Dorigo, M.: On the performance of ant-based clustering. In: Proceedings of the 3rd International Conference on Hybrid Intelligent Systems, Design and Application of Hybrid Intelligent Systems, pp. 204–213. IOS Press, Amsterdam (2003)Google Scholar
  10. 10.
    Liu, S., Dou, Z.T., Li, F., Huang, Y.L.: A new ant colony clustering algorithm based on DBSCAN. In: Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, Shanghai, pp. 1491–1496 (2004)Google Scholar
  11. 11.
    Vizine, A.L., de Castro, L.N., Hruschka, E.R., Gudwin, R.R.: Towards improving clustering ants: an adaptive ant clustering algorithm. Informatica 29, 143–154 (2005)MATHGoogle Scholar
  12. 12.
    Bell, W.J.: Chemo-orientation in walking insects. In: Bell, W.J., Carde, R.T. (eds.) Chemical Ecology of Insects, pp. 93–109 (1984)Google Scholar
  13. 13.
    Ono, M., Igarashi, T., Ohno, E., Sasaki, M.: Unusual thermal defence by a honeybee against mass attack by hornets. Nature 377, 334–336 (1995)CrossRefGoogle Scholar
  14. 14.
    Sukama, M., Fukami, H.: Aggregation arrestant pheromone of the German cockroach, Blattella germanica (L.) (Dictyoptera: Blattellidae): isolation and structure elucidation of blasttellastanoside-A and B. Journal of Chemical Ecology 19, 2521–2541 (1993)CrossRefGoogle Scholar
  15. 15.
    Tsutsui, S.: Ant colony optimization for continuous domains with aggregation pheromones metaphor. In: Proceedings of the 5th International Conference on Recent Advances in Soft Computing (RASC 2004), United Kingdom, pp. 207–212 (2004)Google Scholar
  16. 16.
    Tsutsui, S., Ghosh, A.: An extension of ant colony optimization for function optimization. In: Proceedings of the 5th Asia Pacific Conference on Simulated Evolution and Learning (SEAL 2004), Pusan, Korea (2004)Google Scholar
  17. 17.
    Kothari, M., Ghosh, S., Ghosh, A.: Aggregation pheromone density based clustering. In: Proceedings of 9th International conference on Information Technology, Bhubaneswar, India. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  18. 18.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Elsevier Academic Press, Amsterdam (2003)Google Scholar
  19. 19.
    Halkidi, M., Vazirgiannis, M.: Clustering validity assessment: finding the optimal partitioning of a data set. In: Proceedings of ICDM, California, USA (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Susmita Ghosh
    • 1
  • Megha Kothari
    • 1
  • Ashish Ghosh
    • 2
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.Machine Intelligence Unit and Center for Soft Computing ResearchIndian Statistical InstituteKolkataIndia

Personalised recommendations