Spin Images and Neural Networks for Efficient Content-Based Retrieval in 3D Object Databases

  • Pedro A. de Alarcón
  • Alberto D. Pascual-Montano
  • José M. Carazo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2383)


We describe a system for querying 3D model databases using the spin image representation as a shape signature for objects depicted as triangular meshes. The spin image representation facilitates the task of aligning the query object with respect to matched models (coarse-grain registration). The main contribution of this work is the introduction of a three-level indexing schema based on artificial neural networks. The indexing schema improves significantly the efficiency in matching query spin images against those stored in the database. Our results are suitable for content-based retrieval in 3D general object databases. A particular application to molecular databases is also presented.


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  1. 1.
    Flickner, M. et al: Query by image and video content: the QBIC system. IEEE Computer Vol. 28(9) (1995) 23–32.Google Scholar
  2. 2.
    Gupta, A. et al.: The Virage image search engine: an open framework for image management. Storage and Retrieval for Image and Video Databases IV, Proc SPIE 2670 (1996) 76–87.Google Scholar
  3. 3.
    Sussman, Lin, Jiang, Manning, Prilusky, Ritter and Abola: Protein Data Bank (PDB): database of 3D structural information of biological macromolecules. Acta Crystallogr. Sect D 54 (1998) 1078–1084.CrossRefGoogle Scholar
  4. 4.
    Paquet, E., Rioux, M.: Nefertiti: a tool for 3-D shape databases management, Proceedings of the SAE International Conference on Digital Human Modeling for Design and Engineering, The Hague, Netherlands. (1999)Google Scholar
  5. 5.
    Elvins, T., Jain, R.: Web-based volumetric data retrieval. Symposium on Virtual Reality Modeling Language, San Diego USA. (1995) 7–12.Google Scholar
  6. 6.
    Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Matching 3D Models with Shape Distributions. Shape Modeling International, Genova, Italy, May, 2001.Google Scholar
  7. 7.
    Liu, Y., Rothfus, W. E., Kanade, T.: Content-based 3D Neuroradiologic Image Retrieval: Preliminary Results. IEEE International Workshop on Content-based Access of Image and Video Databases. (1998) 91–100.Google Scholar
  8. 8.
    Ankerst M., Kastenmüller G., Kriegel H.-P., Seidl T.:Nearest Neighbor Classification in 3D Protein Databases. Proc. 7th Int. Conf. on Intelligent Systems for Molecular Biology (ISMB), Heidelberg, Germany. AAAI Press (1999) 34–43.Google Scholar
  9. 9.
    Johnson, A., Hebert, M.: Recognizing objects by matching oriented points. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’ 97) (1997) 684–689.Google Scholar
  10. 10.
    Besl, P. J.: The free-form surface matching problem. Machine Vision for Three-dimensional Scenes. Academic Press, San Diego (1990) 25–71.Google Scholar
  11. 11.
    Hebert, M., Ponce, J., Boult and Gross, A.: Object Representation in Computer Vision. Springer-Verlag Eds., Berlin 1995.MATHGoogle Scholar
  12. 12.
    Campbell, R. J., Flynn, P. J.: A Survey of Free-Form Object Representation and Recognition Techniques. Computer Vision and Image Understanding, Vol 1 (2001) 166–210.CrossRefGoogle Scholar
  13. 13.
    Loncaric, S.: A survey of shape analysis techniques. Pattern recognition, Vol 31(8) (1998) 983–1001.CrossRefGoogle Scholar
  14. 14.
    Veltkamp, R. C., Hagedoorn, M.: State of the art in shape matching. Technical Report UU-CS-1999-27, Utretch University, the Netherlands, 1999.Google Scholar
  15. 15.
    Johnson, A., Hebert, M.: Efficient multiple model recognition in cluttered 3-D scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’ 98) (1998) 671–677.Google Scholar
  16. 16.
    Böhm C, Kriegel H.-P., Seidl T.: Adaptable Similarity Search Using Vector Quantization. Proc. Int. Conf. on Data Warehousing and Knowledge Discovery (DaWaK 2001), Munich, Germany, (2001).Google Scholar
  17. 17.
    Bernard, S., Boujemaa, N., Vitale, D., Bricot, C: Fingerprint Classification using Kohonen Topologic Map. The IEEE International Conference On Image Processing, ICIP (2001).Google Scholar
  18. 18.
    Kohonen T.: Self-Organizing maps, Second Edition, Springer-Verlag Eds (1997).Google Scholar
  19. 19.
    Pascual-Montano, A., Donate, L. E., Valle, M., Bárcena, M., Pascual-Marqui, R. D., Carazo, J. M.: A Novel Neural Network Technique for Analysis and Classification of EM Single Particle Images. J. of Struct. Biol. 133(2/3), (2001) 233–245.CrossRefGoogle Scholar
  20. 20.
    De-Alarcón, P. A., Pascual-Montano, A., Gupta, A., Carazo, J. M.:Modeling shape and topology of low-resolution density maps of biological macromolecules. (2002) (Biophysical Journal, in press).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Pedro A. de Alarcón
    • 1
  • Alberto D. Pascual-Montano
    • 1
  • José M. Carazo
    • 1
  1. 1.Biocomputing Unit. Centro Nacional de Biotecnología (CSIC)MadridSpain

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