A Novel Approach for Accessing Partially Indexed Image Corpora

  • Gérald Duffing
  • Malika SmaÏl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)


This paper addresses the issue of efficient retrieval from image corpora in which only a little proportion is thematically indexed. We propose a hybrid approach integrating thematic querying/search with content-based retrieval. We show how a preliminary double clustering of image corpus exploited by an adapted retrieval process constitutes an answer to the pursued objective. The retrieval process takes advantage of user-system interaction via relevance feedback mechanism whose results are integrated in a virtual image. Some experimental results are provided and discussed to demonstrate the effectiveness of this work.


Image Retrieval Retrieval Process Relevant Image Virtual Image Cosine Measure 
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.
    P. Aigrain, H. Zhang, and D. Petkovic. Content-Based Representation and Retrieval of Visual Media: A State-of-the-Art Review. Multimedia Tools and Applications, 3:179–202, 1996.CrossRefGoogle Scholar
  2. 2.
    G. Ciocca and R. Schettini. A relevance feedback mechanism for content-based image retrieval. Information Processing and Management, 35:605–632, 1999.CrossRefGoogle Scholar
  3. 3.
    R. Duda and P. Hart. Pattern Classification and Scene Analysis. John Wiley and Sons, 1973.Google Scholar
  4. 4.
    C. Fellbaum, editor. WORDNET: An Electronic Lexical Database. MIT Press, 1998.Google Scholar
  5. 5.
    C. Goble, M. O’Docherty, P. Crowther, M. Ireton, J. Oakley, and C. Xydeas. The Manchester Multimedia Information System. In Lecture Notes in Computer Science, vol. 580, pages 39–55. Springer, 1992.Google Scholar
  6. 6.
    A. Gupta, T. Weymouth, and R. Jain. Semantic queries with pictures: the VIMSYS model. In Proceedings of the 17th int. conf. on Very Large Data Bases, pages 69–79, Barcelona, septembre 1991.Google Scholar
  7. 7.
    G. Halin. Machine Learning and Vectorial Matching for an Image Retrieval Model: EXPRIM and the System RIVAGE. In J.-L. Vidick, editor, A CM 13th Int. Gonf. on Research and Development in Information Retrieval, pages 99–114, Brussels (Belgium), septembre 1990. Presses Universitaires de Bruxelles.Google Scholar
  8. 8.
    R.M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Trans, on Systems, Man, and Cybernetics, SMC-3(6):610–621, 1973.CrossRefGoogle Scholar
  9. 9.
    N. Jardine and C.J. van Rijsbergen. The use of hierarchical clustering in information retrieval. Information Storage and Retrieval, 7:217–240, 1971.CrossRefGoogle Scholar
  10. 10.
    T. Kato. Database architecture for content-based image retrieval. In Image Storage and Retrieval Systems, volume 1662, pages 112–123, San Jose, CA, 1992. SPIE.Google Scholar
  11. 11.
    C. Nastar, M. Mischke, C. Meilhac, N. Boudjemaa, H. Bernard, and M. Mautref. Retrieving images by content: the surfimage system. In Multimedia Information Systems, Istanbul, 1998.Google Scholar
  12. 12.
    W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Taubin. The QBIC project: querying images by content using color, texture and shape. In Wayne Niblack, editor, Storage and Retrieval for Image and Video Databases, pages 173–181, San Jose, CA, 1993. SPIE.Google Scholar
  13. 13.
    V. E. Ogle and M. Stonebraker. CHABOT: Retrieval from a relational database of images. IEEE Computer, 28(9):40–48, 1995.Google Scholar
  14. 14.
    R.W. Picard and T.P. Minka. Vision Texture for Annotation. Multimedia Systems, 3:3–14, 1995.CrossRefGoogle Scholar
  15. 15.
    W.K. Pratt. Digital Image Processing. John Wiley & Sons, New York, second edition, 1991.Google Scholar
  16. 16.
    F. Rabitti and P. Savino. Querying semantic image database. In Image Storage and Retrieval Systems, volume 1662, pages 69–78, San Jose, CA, 1992. SPIE.Google Scholar
  17. 17.
    C.J. vanRijsbergen and W.B. Croft. Document clustering: an evaluation of some experiments with the Cranfield 1400 collection. Information processing and management, 11:171–182, 1974.CrossRefGoogle Scholar
  18. 18.
    G. Salton and M.J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.Google Scholar
  19. 19.
    M. SmaÏl. Case-Base Reasoning Meets Information Retrieval. In RIAO 94-’ Intelligent Multimedia Information Retrieval Systems and Management, page 133, 1994.Google Scholar
  20. 20.
    J. R. Smith and S.-F. Chang. Querying by color regions using the VisualSEEk content-based visual query system. In Mark T. Maybury, editor, Intelligent Multimedia Information Retrieval, pages 23–41. AAAI Press Menlo Park, 1997.Google Scholar
  21. 21.
    M.J. Swain and D.H. Ballard. Color indexing. International Journal of Computer Vision, 7(l):ll–32, 1991.Google Scholar
  22. 22.
    E. Voorhees. The Effectiveness and Efficiency of Agglomerative Hierarchic Clustering in Document Retrieval. PhD thesis, Cornell University, Ithaca, NY, Etats-Unis, 1985. Rapport Technique TR 85–705.Google Scholar
  23. 23.
    T. Whalen, E.S. Lee, and F. Safayeni. The Retrieval of Images from Image Databases. Behaviour & Information Technology, 14(1):3–13, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Gérald Duffing
    • 1
  • Malika SmaÏl
    • 1
  1. 1.Campus Sciences BP 239UMR 7503 LORIAFrance

Personalised recommendations