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

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Abstract

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.

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Notes

  1. Note that superscript \((t)\) indicates computations in iteration \(t\).

  2. The content language indicates the syntax used to express the content.

  3. The ontology indicates the vocabulary of the symbols used in the content.

  4. Images are stored in a physical storage such as hard disk.

  5. The online documentation is available at http://jade.tilab.com.

  6. FIPA: the Foundation for Intelligent Physical Agents.

References

  1. Agogino, A., Tumer, K.: Efficient agent-based cluster ensembles. In: Proceedings of 5th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’06), pp. 1079–1086. ACM (2006)

  2. Al-Zoubi, M.B., Hudaib, A., Al-Shboul, B.: A fast fuzzy clustering algorithm. In: Proceedings of 6th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED’07), vol. 6, pp. 28–32. World Scientific and Engineering Academy and Society (WSEAS) (2007)

  3. Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE. Wiley, New York (2007)

  4. Bellifemine, F., Poggi, A., Rimassa, G.: JADE—a FIPA-compliant agent framework. Tech. rep., Telecom Italia Internal (1999). http://jade.cselt.it/papers/PAAM.pdf (2014). Accessed 28 Nov 2014

  5. Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation: Numerical Methods. Prentice-Hall, Inc., USA (1989)

  6. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy \(c\)-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)

    Article  Google Scholar 

  7. Chaimontree, S., Atkinson, K., Conenen, F.: A Multi-Agent Based Approach to Clustering: Harnessing the Power of Agents. Agents and Data Mining Interactions. Lecture Notes in Computer Science, vol. 7103, pp. 16–29. Springer, Berlin (2012)

  8. Chen, Y., Wang, J.Z., Krovetz, R.: CLUE: cluster-based retrieval of images by unsupervised learning. IEEE Trans. Image Process. 14(8), 1187–1201 (2005)

    Article  Google Scholar 

  9. Chitsaz, M., Seng, W.: Medical image segmentation using a multi-agent system approach. Int. Arab J. Inf. Technol. 10(3), 222–229 (2013)

    Google Scholar 

  10. Chuang, K.S., Tzeng, H.L., Chen, S., Wu, J., Chen, T.J.: Fuzzy \(c\)-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30, 9–16 (2006)

    Article  Google Scholar 

  11. Dimitriadis, S., Marias, K., Orphanoudakis, S.C.: A multiagent platform for content based image retrieval. Multimed. Tools Appl. 33(1), 57–72 (2007)

    Article  Google Scholar 

  12. Eschrich, S., Ke, J., Hall, L.O., Goldgof, D.B.: Fast accurate fuzzy clustering through data reduction. IEEE Trans. Fuzzy Syst. 11(2), 262–270 (2003)

    Article  Google Scholar 

  13. FIPA: FIPA. The Foundation for Intelligent Physical Agents (1999). http://www.fipa.org (2014). Accessed 28 Nov 2014

  14. Gen-yuan, D., Fang, M., Sheng-li, T., Xi-rong, G.: Remote sensing image sequence segmentation based on the modified fuzzy \(c\)-means. J. Softw. 5(1), 28–35 (2010)

    Google Scholar 

  15. Ghosh, S., Dubey, S.K.: Comparative analysis of \(k\)-means and fuzzy \(c\)-means algorithms. Int. J. Adv. Comput. Sci. Appl. 4(4), 35–39 (2013)

  16. HongLei, Y., JunHuan1, P., BaiRu, X., DingXuan1, Z.: Remote sensing classification using fuzzy \(c\)-means clustering with spatial constraints based on markov random field. Eur. J. Remote Sens. 46, 305–316 (2013)

  17. Hung, M.C., Yang, D.L.: An Efficient fuzzy \(c\)-means clustering algorithm. In: Proceedings of IEEE International Conference on Data Mining (ICDM’01), pp. 225–232. IEEE Computer Society (2001)

  18. Imianvan, A.A., Obi, J.C.: Fuzzy cluster means expert system for the diagnosis of tuberculosis. Glob. J. Comput. Sci. Technol. 11(6), 41–48 (2011)

    Google Scholar 

  19. Kelash, H., El\_Dein, M.Z.G., Kamel, N.: Agent distribution based systems for parallel image processing. In: Proceedings of 1st International Conference on Graphics Vision and Image Processing (GVIP’05), vol. 05, pp. 59–64. ICGST, Cairo (2005)

  20. Keshtkar, F., Gueaieb, W., White, A.: An agent-based model for image segmentation. In: Proceedings of 13th Multi-disciplinary Iranian Researchers Conference in Europe. Leeds, UK (2005)

  21. Kiselev, I., Alhajj, R.: Self-organizing multi-agent system for adaptive continuous unsupervised learning in complex uncertain environments. In: Proceedings of 23rd National Conference on Artificial intelligence (AAAI’08), vol. 3, pp. 1808–1809. AAAI Press (2008)

  22. Kwok, T., Smith, K., Lazano, S., Taniar, D.: Parallel fuzzy \(c\)-means clustering for large data sets. In: Euro-Par Proceedings of 8th International Conference on Parallel Processing. Lecture Notes in Computer cience, vol. 2400, pp. 365–374. Springer, New York (2002)

  23. Lotfy, H.M., Elmaghraby, A.S.: A novel cluster-based image retrieval. In: Proceedings of the 4th IEEE International Symposium on Signal Processing and Information Technology, pp. 338–341 (2004)

  24. Modenesi, M.V., Costa, M.C.A., Evsukoff, A.G., Ebecken, N.F.F.: Parallel fuzzy \(c\)-means cluster analysis. In: VECPAR Proceedings of 7th International Conference on High Performance Computing for Computional Science. Lecture Notes in Computer Science, vol. 4395, pp. 52–65. Springer, New York (2006)

  25. Mohamed, N.A., Ahmed, M.N., Farag, A.: Modified fuzzy \(c\)-mean in medical image segmentation. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3429–3432 (1999)

  26. Ooi, W.S., Lim, C.P.: A fuzzy clustering approach to content-based image retrieval. In: Proceedings of Post ICONIP’09 Workshop on Advances in Intelligent Computing, pp. 11–16, Kuala Lumpur (2009)

  27. Pooja, Srivastava, N., Shukla, K.K., Singhal, A.: Agent based image segmentation methods: a review. Int. J. Comput. Technol. Appl. 2(3), 704–708 (2011)

  28. Rahimi, S., Zargham, M., Thakre, A., Chhillar, D.: A parallel fuzzy \(c\)-mean algorithm for image segmentation. In: IEEE Annual Meeting of the Fuzzy Information Processing Society (NAFIPS’04), vol. 1, pp. 234–237. IEEE (2004)

  29. Reddi, K.: Integrating fuzzy \(c\)-means clustering technique with \(k\)-means clustering technique for CBIR. Int. J. Comput. Distrib. Syst. 3(3), 1–7 (2013)

    Google Scholar 

  30. Reed, J.W., Potok, T.E., Patton, R.M.: A multi-agent system for distributed cluster analysis. In: Proceedings of 3rd International Workshop on Software Engineering for Large-Scale Multi-Agent Systems (SELMAS’04), W16L Workshop-26th International Conference on Software Engineering, pp. 152–155 (2004)

  31. Saha, S., Sen, S.: Agent based framework for content based image retrieval. In: Proceedings of AAAI Spring Symposium on Interaction Between Humans and Autonomous Systems over Extended Operation. Stanford University, USA (2004)

  32. Shambharkar, S., Tirpude, S.: Fuzzy \(c\)-means clustering for content based image retrieval system. In: International Conference on Advancements in Information Technology with Workshop of ICBMG’11, International Proceedings of Computer Science and Information Technology IPCSIT, vol. 20, pp. 148–152 (2011)

  33. da Silva, J.C., Klusch, M., Lodi, S., Moro, G.: Privacy-preserving agent-based distributed data clustering. Web Intell Agent Syst 4(2), 221–238 (2006)

    Google Scholar 

  34. Suganya, R., Shanthi, R.: Fuzzy \(c\)-means algorithm—a review. Int. J. Sci. Res. Publ. 2(11) (2012)

  35. Wang, X.Y., Garibaldi, J., Ozen, T.: Application of the fuzzy \(c\)-means clustering method on the analysis of non pre-processed FTIR data for cancer diagnosis. In: Proceedings of 8th Australian and New Zealand Conference on Intelligent Information Systems, pp. 233–238 (2003)

  36. Weiss, G.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, USA (1999)

  37. Yang, J., Watada, J.: Fuzzy clustering analysis of data mining: application to an accident mining system. Int. J. Innov. Comput. Inf. Control 8(8), 5715–5724 (2012)

    Google Scholar 

  38. Yang, M.S.: A survey of fuzzy clustering. Math. Comput. Model. 18(11), 1–16 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  39. Yang, Y., Huang, S.: Image segmentation by fuzzy \(c\)-means clustering algorithm with a novel penalty term. Comput. Inform. 26, 17–31 (2007)

    MATH  Google Scholar 

  40. Yang, Y., Zheng, C., Lin, P.: Fuzzy \(c\)-means clustering algorithm with a novel penalty term for image segmentation. Opto-Electron. Rev. 13(4), 309–315 (2005)

    Google Scholar 

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Acknowledgments

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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Authors and Affiliations

  1. Mathematics Department, Faculty of Science, Ain Shams University, Cairo, 11566, Egypt

    Nashwa M. Abdelghaffar, Hewayda M. S. Lotfy & Soheir M. Khamis

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  1. Nashwa M. Abdelghaffar
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  2. Hewayda M. S. Lotfy
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  3. Soheir M. Khamis
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Correspondence to Nashwa M. Abdelghaffar.

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Abdelghaffar, N.M., Lotfy, H.M.S. & Khamis, S.M. A multi-agent-based approach for fuzzy clustering of large image data. J Real-Time Image Proc 15, 235–247 (2018). https://doi.org/10.1007/s11554-014-0473-3

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