Journal of Real-Time Image Processing

, Volume 15, Issue 2, pp 235–247 | Cite as

A multi-agent-based approach for fuzzy clustering of large image data

  • Nashwa M. Abdelghaffar
  • Hewayda M. S. Lotfy
  • Soheir M. Khamis
Original Research Paper


Data clustering usually requires extensive computations of similarity measures between dataset members and cluster centers, especially for large datasets. Image clustering can be an intermediate process in image retrieval or segmentation, where a fast process is critically required for large image databases. This paper introduces a new approach of multi-agents for fuzzy image clustering (MAFIC) to improve the time cost of the sequential fuzzy \(c\)-means algorithm (FCM). The approach has the distinguished feature of distributing the computation of cluster centers and membership function among several parallel agents, where each agent works independently on a different sub-image of an image. Based on the Java Agent Development Framework platform, an implementation of MAFIC is tested on 24-bit large size images. The experimental results show that the time performance of MAFIC outperforms that of the sequential FCM algorithm by at least four times, and thus reduces the time needed for the clustering process.


Image clustering Fuzzy \(c\)-means Multi-agent system  



The authors would like to thank the anonymous reviewers for their precious comments and suggestions which greatly helped in improving the presentation quality of the manuscript.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Nashwa M. Abdelghaffar
    • 1
  • Hewayda M. S. Lotfy
    • 1
  • Soheir M. Khamis
    • 1
  1. 1.Mathematics Department, Faculty of ScienceAin Shams UniversityCairoEgypt

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