Advertisement

Interaction in Content-Based Image Retrieval: An Evaluation of the State-of-the-Art

  • Marcel Worring
  • Arnold Smeulders
  • Simone Santini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)

Abstract

The paper presents a panoramic view of the work carried out in the last few years in the area of interactive content-based image retrieval. We define a unifying framework based on query space in which exisiting methods are described. The framework allows to evaluate methods in their proper context. An important part of the framework is a classification of different query types which helps in bridging the gap between desired functionality and efficient support in an actual system. Having put methods in a common framework gives way to indentify promising research directions that have not yet been sufficiently explored.

Keywords

Image Retrieval Image Query Query Result Tile System Target Search 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    C. Carson, S. Belongie, H. Greenspan, and J. Malik. Region-based image querying. In Proceedings of the IEEE International Workshop on Content-Based Access of Image and Video Databases, 1997.Google Scholar
  2. 2.
    G. Ciocca and R Schettini. Using a relevance feedback mechanism to improve content-based image retrieval. In Proceedings of Visual Information and Information Systems, pages 107–114, 1999.Google Scholar
  3. 3.
    I. J. Cox, M. L. Miller, T. P. Minka, and T. V. Papathomas. The bayesian image retrieval system, PicHunter: theory, implementation, and pychophysical experiments. IEEE Transactions on Image Processing, 9(l):20–37, 2000.CrossRefGoogle Scholar
  4. 4.
    A. delBimbo and P. Pala. Visual image retrieval by elastic matching of user sketches. IEEE Transactions on PAMI, 19(2):121–132, 1997.Google Scholar
  5. 5.
    M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: the QBIC system. IEEE Computer, 1995.Google Scholar
  6. 6.
    T. Gevers and A. W. M. Smeulders. Pictoseek: combining color and shape invariant features for image retrieval. IEEE Transactions on Image Processing, 9(l):102–119, 2000.CrossRefGoogle Scholar
  7. 7.
    S. Ghebreab, M. Worring, H.D. Tagare, and C.C. Jaffe. SCHEMed: a visual database tool for definition and entry of medical image data. In R. Jain and S. Santini, editors, Proceedings of Visual Information Systems, pages 189–196. Knowledge Systems Institute, 1997.Google Scholar
  8. 8.
    A. Gupta and R. Jain. Visual information retrieval. Communications of the ACM, 40(5):71–79, 1997.CrossRefGoogle Scholar
  9. 9.
    A. Hiroike, Y. Musha, A. Sugimoto, and Y. Mori. Visualization of information spaces to retrieve and browse image data. InD.P. Huijsmans and A.W.M. Smeulders, editors, Proceedings of Visual 99, International Conference on Visual Information Systems, volume 1614 of Lecture Notes in Computer Science, pages 155–162, 1999.Google Scholar
  10. 10.
    R. Jain, editor. NSF Workshop on Visual Information Management Systems, Redwood, CA, 1992.Google Scholar
  11. 11.
    T. Kakimoto and Y. Kambayashi. Browsing functions in three-dimensional space for digital libraries. International Journal of Digital Libraries, 2:68–78, 1999.CrossRefGoogle Scholar
  12. 12.
    T. Kato, T. Kurita, N. Otsu, and K. Hirata. A sketch retrieval method for full color image database-query by visual example. In Proceedings of the ICPR, Computer Vision and Applications, The Hague, pages 530–533, 1992.Google Scholar
  13. 13.
    L.J. Latecki and R. Lakamper. Contour-based shape similarity. In D.P. Huijsmans and A.W.M. Smeulders, editors, Proceedings of Visual Information and Information Systems, number 1614 in Lecture Notes in Computer Science, pages 617–624, 1999.Google Scholar
  14. 14.
    M. Lee M. L. Pao. Concepts of Information Retrieval. Libraries Unlimited Englewood, Colo, 1989.Google Scholar
  15. 15.
    C. Meilhac and C. Nastar. Relevance feedback and category search in image databases. In IEEE International Conference on Multimedia Computing and Systems, pages 512–517, 1999.Google Scholar
  16. 16.
    T. P. Minka and R. W. Picard. Interactive lerning with a “society of models.”. Pattern Recognition, 30(4):565–582, 1997.CrossRefGoogle Scholar
  17. 17.
    V. E. Ogle. CHABOT-retrieval from a relational database of images. IEEE Computer, 28(9):40–48, 1995.Google Scholar
  18. 18.
    Y. Rui, T.S. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on circuits and video technology, 1998.Google Scholar
  19. 19.
    H. Samet and A. Soffer. MARCO: MAp Retrieval by COntent. IEEE Transactions on PAMI, 18(8):783–798, 1996.Google Scholar
  20. 20.
    S. Santini, A. Gupta, and R. Jain. Emergent semantics through interaction in image databases. IEEE Transactions on Knowledge and Data Engineering, (in press).Google Scholar
  21. 21.
    S. Sclaroff. Deformable prototypes for encoding shape categories in image databases. Pattern Recognition, 30(4):627–641, 1997.CrossRefGoogle Scholar
  22. 22.
    C-R. Shyu, C.E. Brodley, A.C. Kak, and A. Kosaka. ASSERT: a physician in the loop content-based retrieval system for HCRT image databases. Image Understanding, 75(1/2):111–132, 1999.CrossRefGoogle Scholar
  23. 23.
    A.W.M. Smeulders, S.D. Olabariagga, R. van denBoomgaard, and m Worring. Interactive segmentation. In R. Jain and S. Santini, editors, Proceedings of Visual Information Systems, pages 5–12. Knowledge Systems Institute, 1997.Google Scholar
  24. 24.
    A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content based retrieval at the end of the early years. Submitted to Pattern Analysis and Machine Intelligence.Google Scholar
  25. 25.
    J. Smith and S-F. Chang. Visually searching the WEB for content. IEEE Multimedia, 4(3):12–20, 1997.CrossRefGoogle Scholar
  26. 26.
    J. R. Smith and S-F. Chang. Integrated spatial and feature image query. Multimedia systems, 7(2):129–140, 1999.CrossRefGoogle Scholar
  27. 27.
    A. Vailaya, M. Figueiredo, A. Jain, and H. Zhang. Content-based hierarchical classification of vacation images. In IEEE International Conference on Multimedia Computing and Systems, 1999.Google Scholar
  28. 28.
    J. Vendrig, M. Worring, and A.W.M. Smeulders. Filter image browsing: exploiting interaction in retrieval. In D.P. Huijsmans and A.W.M. Smeulders, editors, Proceedings of Visual Information and Information Systems, volume 1614 of Lecture Notes in Computer Science, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Marcel Worring
    • 1
  • Arnold Smeulders
    • 1
  • Simone Santini
    • 2
  1. 1.University of AmsterdamIntelligent Sensory Information SystemsThe Netherlands
  2. 2.University of California San DiegoCalifornia

Personalised recommendations