Retrieving Images by Content: The Surfimage System

  • Chahab Nastar
  • Matthias Mitschke
  • Nozha Boujemaa
  • Christophe Meilhac
  • Héléne Bernard
  • Marc Mautref
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1508)


Surfimage is a versatile content-based image retrieval system allowing both efficiency and flexibility, depending on the application. Surfimage uses the query-by-example approach for retrieving images and integrates advanced features such as image signature combination, multiple queries, query refinement, and partial queries. The classic and advanced features of Surfimage are detailed hereafter. Surfimage has been extensively tested on dozens of databases, demonstrating performance and robustness. Several experimental results are presented in the paper.


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  1. 1.
    S. Belongie, C. Carson, H. Greenspan, and J. Malik. Color-and texture-based image segmentation using em and its application to content-based image retrieval. In Proceedings of the Sixth International Conference on Computer Vision (ICCV’ 98), Bombay, January 1998.Google Scholar
  2. 2.
    I. Cox et al. PicHunter: Bayesian relevance feedback for image retrieval. In Proceedings of 13th International Conference on Pattern Recognition, Vienna, Austria, 1996.Google Scholar
  3. 3.
    M. Flickner et al. Query by image and video content: the qbic system. IEEE Computer, 28(9), 1995.Google Scholar
  4. 4.
    A. Jain and A. Vailaya. Image retrieval using color and shape. Pattern Recognition, 29(8), 1996.Google Scholar
  5. 5.
    T. Minka and R. Picard. Interactive learning using a society of models. Pattern Recognition, 30(4), 1997.Google Scholar
  6. 6.
    H. Murase and S. K. Nayar. Visual learning and recognition of 3D objects from appearance. International Journal of Computer Vision, 14(5), 1995.Google Scholar
  7. 7.
    C. Nastar and M. Mitschke. Real-time face recognition using feature combination. In 3rd IEEE International Conference on Automatic Face-and Gesture-Recognition (FG’98), Nara, Japan, April 1998.Google Scholar
  8. 8.
    C. Nastar, M. Mitschke, and C. Meilhac. Efficient query refinement for image retrieval. In Computer Vision and Pattern Recognition (CVPR’ 98), Santa Barbara, June 1998.Google Scholar
  9. 9.
    C. Nastar, M. Mitschke, C. Meilhac, and N. Boujemaa. Surfimage: a flexible content-based image retrieval system. In ACM-Multimedia 1998, Bristol, England, September 1998.Google Scholar
  10. 10.
    C. Nastar, B. Moghaddam, and A. Pentland. Flexible images: Matching and recognition using learned deformations. Computer Vision and Image Understanding, 35(2), February 1997.Google Scholar
  11. 11.
    M. Ortega, Y. Rui, K. Chakrabarti, S. Mehrotra, and T. Huang. Supporting similarity queries in MARS. In ACM Multimedia, Seattle, November 1997.Google Scholar
  12. 12.
    A. Pentland, R. Picard, and S. Sclaroff. Photobook: Tools for content-based manipulation of image databases. Int. Journal of Comp. Vision, 18(3), 1996.Google Scholar
  13. 13.
    R. Picard, T. Minka, and M. Szummer. Modeling subjectivity in image libraries. In IEEE Int. Conf. on Image Proc., Lausanne, September 1996.Google Scholar
  14. 14.
    Y. Rui, T. Huang, S. Mehrotra, and M. Ortega. A relevance feedback architecture for content-based multimedia information systems. In Workshop on Content Based Access of Image and Video Libraries, Porto Rico, June 1997.Google Scholar
  15. 15.
    M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 1991.Google Scholar
  16. 16.
    A. Vellaikal and C. Kuo. Joint spatial-spectral indexing of jpeg compressed data for image retrieval. In Int’l Conf. on Image Proc., Lausanne, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Chahab Nastar
    • 1
  • Matthias Mitschke
    • 1
    • 3
  • Nozha Boujemaa
    • 1
  • Christophe Meilhac
    • 1
  • Héléne Bernard
    • 2
  • Marc Mautref
    • 2
  1. 1.INRIA RocquencourtLe ChesnayFrance
  2. 2.Alcatel Corporate Research CenterMarcoussisFrance
  3. 3.Siemens AGErlangenGermany

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