Image Data Source Selection Using Gaussian Mixture Models

  • Soufyane El Allali
  • Daniel Blank
  • Wolfgang Müller
  • Andreas Henrich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4918)


In peer-to-peer (P2P) networks, computers with equal rights form a logical (overlay) network in order to provide a common service that lies beyond the capacity of every single participant. Efficient similarity search is generally recognized as a frontier in research about P2P systems. In literature, a variety of approaches exist. One of which is data source selection based approaches where peers summarize the data they contribute to the network, generating typically one summary per peer. When processing queries, these summaries are used to choose the peers (data sources) that are most likely to contribute to the query result. Only those data sources are contacted.

In this paper we use a Gaussian mixture model to generate peer summaries using the peers’ local data. We compare this method to other local unsupervised clustering methods for generating peer summaries and show that a Gaussian mixture model is promising when it comes to locally generated summaries for peers without the need for a distributed summary computation that needs coordination between peers.


Indexing Data Gaussian Mixture Model Image Retrieval Query Image Relevance Feedback 
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.
    Bender, M., Michel, S., Triantafillou, P., Weikum, G., Zimmer, C.: Minerva: collaborative P2P search. In: VLDB 2005: Proc. of the 31st Intl. Conf. on Very large data bases. VLDB Endowment, pp. 1263–1266 (2005)Google Scholar
  2. 2.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Communications of the ACM 13(7) (1970)Google Scholar
  3. 3.
    Callan, J.P., Lu, Z., Croft, W.B.: Searching distributed collections with inference networks. In: Proc. 18th ACM SIGIR, Seattle, Washington (1995)Google Scholar
  4. 4.
    Chan, P.K.-W.: An extensible meta-learning approach for scalable and accurate inductive learning. PhD thesis, Sponsor-Salvatore J. Stolfo (1996)Google Scholar
  5. 5.
    Clarke, I., Sandberg, O., Wiley, B., Hong, T.W.: Freenet: A distributed anonymous information storage and retrieval system. In: Federrath, H. (ed.) Designing Privacy Enhancing Technologies. LNCS, vol. 2009. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Cuenca-Acuna, F.M., Nguyen, T.: Text-based content search and retrieval in ad hoc P2P communities. Technical Report DCS-TR-483, Department for Computer Science, Rutgers University (2002)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. Wiley-Interscience (2001)Google Scholar
  8. 8.
    Eisenhardt, M., Müller, W., Henrich, A.: Classifying documents by distributed P2P clustering, 286–291 (2003)Google Scholar
  9. 9.
    Eisenhardt, M., Müller, W., Henrich, A., Blank, D., El Allali, S.: Clustering-based source selection for efficient image retrieval in peer-to-peer networks. In: IEEE MIPR 2007, pp. 823–830 (2006)Google Scholar
  10. 10.
    El Allali, S., Blank, D., Eisenhardt, M., Henrich, A., Müller, W.: Untersuchung des Einflusses verschiedener Bild-Features und Distanzmaße im inhaltsbasierten P2P Information Retrieval. In: BTW 2007, 12th GI-Fachtagung für Datenbanksysteme in Business, Technologie und Web (2007)Google Scholar
  11. 11.
    Gravano, L., García-Molina, H., Tomasic, A.: Gloss: text-source discovery over the internet. ACM Trans. Database Syst. 24(2), 229–264 (1999)CrossRefGoogle Scholar
  12. 12.
    Kronfol, A.Z.: A Fault-tolerant, Adaptive, Scalable, Distributed Search Engine. Final Thesis, Princeton (May 2002),
  13. 13.
    Müller, W., Eisenhardt, M., Henrich, A.: Scalable summary based retrieval in P2P networks. In: CIKM 2005: Proc. of the 14th ACM Intl. Conf. on Information and knowledge management, pp. 586–593. ACM Press, New York (2005)CrossRefGoogle Scholar
  14. 14.
    Müller, W., Henrich, A., Eisenhardt, M.: Aspects of adaptivity in P2P information retrieval. In: The 4th International Workshop on Adaptive Multimedia Retrieval AMR 2006 (2006)Google Scholar
  15. 15.
    Nejdl, W., Wolpers, M., Siberski, W., Schmitz, C., Schlosser, M., Brunkhorst, I., Löser, A.: Super-peer-based routing and clustering strategies for rdf-based peer-to-peer networks. In: Proc. of the Intl. World Wide Web Conf. (2003)Google Scholar
  16. 16.
    Qian, F., Li, M., Zhang, L., Zhang, H.-J., Zhang, B.: Gaussian mixture model for relevance feedback in image retrieval. In: IEEE International Conference on Multimedia and Expo, 2002. ICME 2002 (2002)Google Scholar
  17. 17.
    Ratnasamy, S., Francis, P., Handley, M., Karp, R., Schenker, S.: A scalable content-addressable network. In: Proc. 2001 Conf. on applications, technologies, architectures, and protocols for computer communications, San Diego, CA, United States (2001)Google Scholar
  18. 18.
    Sahin, O.D., Gulbeden, A., Emekci, F., Agrawal, D., Abbadi, A.E.: PRISM: indexing multi-dimensional data in P2P networks using reference vectors. In: Proc. of the 13th annual ACM Intl. Conf. on Multimedia, pp. 946–955. ACM Press, New York (2005)CrossRefGoogle Scholar
  19. 19.
    Stoica, I., Morris, R., Karger, D., Kaashoek, F., Balakrishnan, H.: Chord: A scalable Peer-To-Peer lookup service for internet applications. In: Proc. ACM SIGCOMM Conf., San Diego, CA, USA (2001)Google Scholar
  20. 20.
    Tang, C., Xu, Z., Mahalingam, M.: pSearch: Information retrieval in structured overlays. In: First Workshop on Hot Topics in Networks (HotNets-I). Princeton, NJ (2002)Google Scholar
  21. 21.
    Vasconcelos, N.: Bayesian Models for Visual Information Retrieval. PhD thesis, MIT (June 2000)Google Scholar
  22. 22.
    Yang, B., Garcia-Molina, H.: Designing a super-peer network. In: IEEE Intl. Conf. on Data Engineering (2003)Google Scholar
  23. 23.
    Zhang, L., Lin, F., Zhang, B.: A cbir method based on color-spatial feature. In: IEEE Region 10 Annual International Conference 1999, pp. 166–169 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Soufyane El Allali
    • 1
  • Daniel Blank
    • 1
  • Wolfgang Müller
    • 1
  • Andreas Henrich
    • 1
  1. 1.Faculty of Information Systems and Computer Informatics, Chair of Media InformaticsUniversity of BambergBambergGermany

Personalised recommendations