Content Based Retrieval of VRML Objects — An Iterative and Interactive Approach

  • Michael Elad
  • Ayellet Tal
  • Sigal Ar
Part of the Eurographics book series (EUROGRAPH)


We examine the problem of searching a database of three dimensional objects (given in VRML) for objects similar to a given object. We introduce an algorithm which is both iterative and interactive. Rather than base the search solely on geometric feature similarity, we propose letting the user influence future search results by marking some of the results of the current search as ‘relevant’ or ‘irrelevant’, thus indicating personal preferences. A novel approach, based on SVM, is used for the adaptation of the distance measure consistently with these markings, which brings the ‘relevant’ objects closer and pushes the ‘irrelevant’ objects farther. We show that in practice very few iterations are needed for the system to converge well on what the user “had in mind”.


Support Vector Machine Discrete Computational Geom Computational Geometry Relevance Feedback Content Base Image Retrieval 
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.
    IEEE Computer (1995). special issue on content based image retrieval, 28, 9.Google Scholar
  2. 2.
    Agarwal P.K, Sharir M. and Toledo S. Applications of parametric searching in geometric optimization, Proc. 3rd ACM-SIAM Sympos. Discrete Algorithms, 1992, 72–82.Google Scholar
  3. 3.
    Alt H., Behrends B. and Blömer J. Approrimate matching of polygonal shapes, Proc. 7th Annu. ACM Sympos. Comput. Geom., 1991, 186–193Google Scholar
  4. 4.
    Alt H. and Godau M. Measuring the resemblarice of polygonal curves, 8th ACM Symposium on Computational Geometry (1992), 102–109.Google Scholar
  5. 5.
    Alt H. and L.J. Guibas. Resemblance of Geometric Objects. In J.-R. Sack and J. Urrutia, editors Handbook of Computational Geometry. North-Holland., Amsterdam, 2000.Google Scholar
  6. 6.
    Alt H., Melhorn K, Wagener H. and Welzl E. Congruence, Similarity and symmetries of geometric objects, Discrete Computational Geometry, Vol 3 (1988), 237–256.MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Arkin E.M., Chew L.P., Huttenlocher D.P., Kedem K. and Mitchell J.S.B. An efficiently computable metric for comparing polygonal shapes, IEEE Trans. Pattern Anal. Mach. Intell., 13(3), 1991, 209–216.Google Scholar
  8. 8.
    Arkin E.M., Kedem K, Mitchell J.S.B., Sprinzak J. and Werman M. Matching points into pairwise-disjoint noise regions: combinatorial bounds and algorithms ORSA J. Comput., 4(4), 1992, 375–386.MATHCrossRefGoogle Scholar
  9. 9.
    Atallah M.J. A matching problem in the plane, J. Comput. Systems Sci. 31 (1985), 63–70.MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Bertsekas D.B. Non-Linear Progromming, Athena Scientific, Belmont Massachusetts, 1995.Google Scholar
  11. 11.
    Chew L.P., Dor D., Efrat A. and Kedem K. Geometric pattern matching in ddimensional space, Third European Symposium on Algorithms, ESA ’95 (1995), 264–279.Google Scholar
  12. 12.
    Chew L.P., Goodrich M.T., Huttenlocher D.P., Kedem K, Kleinberg J.M. and Kravets D. Geometric pattern matching under Euclidean motion Comput. Geom. Theory Appl., 7, 1997, 113–124.MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Chew L.P. and Kedem K. Improvements on approximate pattern matching problems, Third Scandinavian Workshop on Algorithm Theory, (1992), 318–325.Google Scholar
  14. 14.
    Cox I.J., Miller M.L., Minka T.P., Papathomas T.V. and Yianilos, P.N. The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments. IEEE Transactions on Image Processing, Vol 9, No 1, January 2000.Google Scholar
  15. 15.
    Cortes C. and Vapnik V. Support Vector Networks, Machine Learning, Vol. 20 No. 3, September 1995, 273–297.MATHGoogle Scholar
  16. 16.
    Duda R.M. and Hart P.E. Pattern Classification and Scene Analysis, Wiley, 1973.MATHGoogle Scholar
  17. 17.
    Hagedoorn M. Overmars M. and Veltkamp R.C. A Robust Affine Invariant Similarity Measure Based on Visibility 16th European Workshop Comput. Geom. 2000, 112–116.Google Scholar
  18. 18.
    Huttenlocher D.P. and Kedem K Computing the minimum Hausdorff distance for point sets under translation, ISAAC, (1996).Google Scholar
  19. 19.
    Huttenlocher D.P., Kedem K. and Kleinberg J. On dynamic Voronoi diagrams the minimum Hausdorff distance for point sets under Euclidean motion in the plane, 8th ACM Symposium on Computational Geometry (1992), 110–119.Google Scholar
  20. 20.
    Huttenlocher D.P., Kedem K and Sharir M. The upper envelope of Voronoi surfaces and its applications, Discrete Computational Geometry, 9, (1993), 267–291.MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Minka T. P. and Picard R. W. Interactive learning using a ”society of models. Pattern Recognition, 30(4), 1997Google Scholar
  22. 22.
    O’Rourke J. and Toussaint G.T. Pattern Recognition. In J. O’Rourke and J. E. Goodman, editors, Discrete and Computational Geometry, pages 797–813. CRC Press., New York, 1997.Google Scholar
  23. 23.
    Paquet E. and Rioux M. A Content Based Serach Engine for VRML Databases, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1998.Google Scholar
  24. 24.
    Rui Y. and Huang T. S. and Chang S-F. Image Retrieval: Current Techniques, Promising Directions, and Open Issues. Journal of Visual Communication and Image Representation, 10, (1), 1999, pp. 39–62.CrossRefGoogle Scholar
  25. 25.
    Rui Y. Huang T. S. and Mehrotta S. Content based image retrieval with relevance feedback in MARS. Proceedings of IEEE ICIP ’97.Google Scholar
  26. 26.
    Schwarz J.T. and Sharir M. Identification of partially obscured objects in two and three dimensions by matching of noisy characteristic curves, Int. J. Robotics Research, 6(2) (1987), 29–44.CrossRefGoogle Scholar
  27. 27.
    Scholkopf B. Support Vector Machines IEEE Intelligent Systems, Vol. 13, No. 4, (July-August, 1998), 18–28Google Scholar
  28. 28.
    Sprinzak J. and Werman M. Exact Point Matching, Proc. Israeli Symposium on AI, Vision and Pattern Recognition, Elsevier Science Publishers. 1990.Google Scholar
  29. 29.
    Toussaint G.T. Computational Geometry and Computer Vision. In B. Melter, A. Rosenfeld and P. Bhattacharya, editors, Vision Geometry, pages 213–224. Amer. Math. Soc., Providence, 1991.Google Scholar
  30. 30.
    Vapnik V. The Nature of Statistical Learning Theory, Springer-Verlag, Ney-York, 1995.MATHGoogle Scholar

Copyright information

© Springer-Verlag Wien 2002

Authors and Affiliations

  • Michael Elad
    • 1
  • Ayellet Tal
    • 2
  • Sigal Ar
    • 2
  1. 1.HP LaboratoriesHaifaIsrael
  2. 2.Department of Electrical EngineeringTechnion — Israel Institute of TechnologyHaifaIsrael

Personalised recommendations