A Multi-agent Model for Image Browsing and Retrieval

  • Hue Cao Hong
  • Guillaume Chiron
  • Alain Boucher
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 457)


This paper presents a new and original model for image browsing and retrieval based on a reactive multi-agent system oriented toward visualization and user interaction. Each agent represents an image. This model simplifies the problem of mapping a high-dimensional feature space onto a 2D screen interface and allows intuitive user interaction. Within a unify and local model, as opposed to global traditional CBIR, we present how agents can interact through an attraction/repulsion model. These forces are computed based on the visual and textual similarities between an agent and its neighbors. This unique model allows to do several tasks, like image browsing and retrieval, single/multiple querying, performing relevance feedback with positive/nagative examples, all with heteregeneous data (image visual content and text keywords). Specific adjustments are proposed to allow this model to work with large image databases. Preliminary results on two image databases show the feasability of this model compared with traditional CBIR.


Multi-Agent System content-based image retrieval attraction/repulsion forces 


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  1. 1.
    Boucher, A., Dang, T.H., Le, T.L.: Classification vs recherche d’information: vers une caractérisation des bases d’images. 12èmes Rencontres de la Société Francophone de Classification (SFC), Montréal (Canada) (2005) (in French)Google Scholar
  2. 2.
    Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised Learning of Semantic Classes for Image Annotation and Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3), 394–410 (2007)CrossRefGoogle Scholar
  3. 3.
    Forgáč, M.R.: Decreasing the Feature Space Dimension by Kohonen Self-Organizing Maps. In: 2nd Slovakian Hungarian Joint Symposium on Applied Machine Intelligence, Herľany, Slovakia (2004)Google Scholar
  4. 4.
    Hu, R., Ruger, S., Song, D., Liu, H., Huang, Z.: Dissimilarity mesures for content-based image retrieval. In: 2008 IEEE International Conference Multimedia and Expo (ICME), Hannover, Germany (2008)Google Scholar
  5. 5.
    Mangiameli, P., Chen, S.K., West, D.: A Comparison of SOM neural network and hierarchical clustering methods. European Journal of Operational Research 93(2), 402–417 (1996)zbMATHCrossRefGoogle Scholar
  6. 6.
    Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1075–1088 (2003)CrossRefGoogle Scholar
  7. 7.
    Moghaddam, B., Tian, Q., Lesh, N., Shen, C., Huang, T.S.: Visualization & User-Modeling for Browsing Personal Photo Libraries. International Journal of Computer Vision 56(1/2), 109–130 (2004)CrossRefGoogle Scholar
  8. 8.
    Nguyen, N.V., Boucher, A., Ogier, J.M., Tabbone, S.: Region-Based Semi-automatic Annotation Using the Bag of Words Representation of the Keywords. In: 5th International Conference on Image and Graphics (ICIG), pp. 422–427 (2009)Google Scholar
  9. 9.
    Nguyen, N.V.: Keyword Visual Representation for Interactive Image Retrieval and Image Annotation. PhD thesis, University of La Rochelle (France) (2011) (in French)Google Scholar
  10. 10.
    Picard, D., Cord, M., Revel, A.: CBIR in distributed databases using a multi-agent system. In: IEEE International Conference on Image Processing, ICIP (2006)Google Scholar
  11. 11.
    Plant, W., Schaefer, G.: Visualising Image Database. In: IEEE International Work-Shop on Multimedia Signal Processing, pp. 1–6 (2009)Google Scholar
  12. 12.
    Renault, V.: Organisation de Société d’Agents pour la Visualisation d’Informations Dynamiques. PhD thesis, University Paris 6, France (2001) (in French)Google Scholar
  13. 13.
    Rubner, Y., Guibas, L.J., Tomasi, C.: The earth movers distance, multi-dimensional scaling, and color-based image retrieval. In: APRA Image Understanding Workshop, pp. 661–668 (1997)Google Scholar
  14. 14.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a Metric for Image Retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)zbMATHCrossRefGoogle Scholar
  15. 15.
    Xiao, X., Dow, E.R., Eberhart, R., Miled, Z.B., Oppelt, R.J.: Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization. In: IEEE International Workshop on High Performance Computational Biology (2003)Google Scholar
  16. 16.
    Laaksonen, J., Koskela, M., Oja, E.: PicSOM – Self-organizing image retrieval with MPEG-7 content descriptors. IEEE Transactions on Neural Networks 13(4), 841–853 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hue Cao Hong
    • 1
    • 2
    • 3
  • Guillaume Chiron
    • 4
  • Alain Boucher
    • 1
    • 2
  1. 1.IFI, MSI teamVietnam National UniversityHanoiVietnam
  2. 2.IRD, UMI 209 UMMISCOVietnam National UniversityHanoiVietnam
  3. 3.Hanoi Pedagogical UniversityVinh PhucVietnam
  4. 4.L3IUniversity of La RochelleLa Rochelle cedex 1France

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