An Ant-Based Approach to Color Reduction

  • Avazeh Tashakkori Ghanbarian
  • Ehasanollah Kabir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)


In this article a method for color reduction based on ant colony algorithm is presented. Generally color reduction involves two steps: choosing a proper palette and mapping colors to this palette. This article is about the first step. Using ant colony algorithm, pixel clusters are formed based on their colors and neighborhood information to make final palette. A comparison between the results of the proposed method and some other methods is presented. There are some parameters in the proposed method which can be set based on user needs and priorities. This increases the flexibility of the method.


Neighborhood Information Pattern Recognition Letter Color Reduction Dissimilarity Function Sparse Area 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Trans. on Systems, Man, and Cybernetics 26(1), 29–41 (1996)CrossRefGoogle Scholar
  2. 2.
    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: Proc. First Conf. on Simulation of Adaptive Behavior, pp. 356–363 (1990)Google Scholar
  3. 3.
    Lumer, E.D., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants. In: Proc. of the Third Int. Conf. on Simulation of Adaptive Behavior: From Animals to Animats, vol. 3, pp. 501–508 (1994)Google Scholar
  4. 4.
    Kuntz, P., Snyers, D.: New Results On An Ant-Based Heuristic for Highlighting The Organization Of Large Graphs. In: Proc. of the 1999 Congress on Evolutionary Computation, pp. 1451–1458 (1999)Google Scholar
  5. 5.
    Handl, J., Meyer, B.: Improved ant-based clustering and sorting in a document retrieval interface. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 913–923. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Monmarché, N.: On Data Clustering With Artificial Ants (AAAI 1999 & GECCO 1999). In: Workshop on Data Mining with Evolutionary Algorithms, pp. 23–26 (1999)Google Scholar
  7. 7.
    Kanade, P.M., Hall, L.O.: Fuzzy Ants As A Clustering Concept. In: 22nd Int. Conf. of the North American Fuzzy Information Processing Society NAFIPS, pp. 227–232 (2003)Google Scholar
  8. 8.
    Labroche, N., Monmarché, N., Venturini, G.: Visual Clustering With Artificial Ants Colonies. In: Seventh Int. Conf. on Knowledge-Based Intelligent Information & Engineering Systems, pp. 332–338 (2003)Google Scholar
  9. 9.
    Scheunders, P.: A comparison of clustering algorithms for color image quantization. Pattern Recognition Letters, 1379–1384 (1997)Google Scholar
  10. 10.
    Bing, Z., Junyi, S., Qinke, P.: An Adjustable Algorithm For Color Quantization. Pattern Recognition Letters 25, 1787–1797 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Avazeh Tashakkori Ghanbarian
    • 1
  • Ehasanollah Kabir
    • 1
  1. 1.Department of Electrical EngineeringTarbiat Modarres UniversityTehranIran

Personalised recommendations