New Generation Computing

, Volume 18, Issue 2, pp 147–156 | Cite as

Photograph retrieval and classification by visual keywords and thesaurus

  • Joo -Hwee Lim
Special Issue


As we collect more digital images with the advent of digital cameras, we need effective content-based search and categorization functions. In this paper, we propose a novel notion of visual keywords to describe and compare digital visual contents. Visual keywords are visual prototypes extracted from a visual content domain with semantics labels. They can be further abstracted to form visual thesaurus. An image is indexed as a spatial distribution of visual keywords. Both retrieval and classification evaluation tasks on professional natural scene photographs have demonstrated the usefulness of this new methodology.


Image Retrieval Image Classification Visual Keywords Visual Thesaurus 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1).
    Carson, C. et al., “Color- and Texture-based Image Segmentation using EM and its Application to Image Query and Classification,” submitted toIEEE Tran. PAMI, 1999.Google Scholar
  2. 2).
    Jain, R., Kasturi, R., and Schunck, B.G.,Machine Vision, McGraw-Hill, Inc., 1995.Google Scholar
  3. 3).
    Lim, J.H., “Learnable Visual Keywords for Image Classification,”Proc. of ACM Digital Libraries’99, ACM Press, 1999.Google Scholar
  4. 4).
    Lim, J.H., “Visual Keywords: From Text IR to Multimedia IR,” to appear inSoft Computing in Information Retrieval: Techniques and Applications (F. Crestani & G. Pasi ed.), Physica-Verlag, Springer Verlag, Germany, 1999.Google Scholar
  5. 5).
    Lipson, P., Grimson, E., and Sinha, P., “Configuration based Scene Classification and Image Indexing,”Proc. of CVPR’97, pp. 1007–1013, 1997.Google Scholar
  6. 6).
    Manjunath, B.S., and Ma, W.Y., “Texture Features for Browsing and Retrieval of Image Data,”IEEE Trans. PAMI, 18, 8, pp. 837–842, 1996.Google Scholar
  7. 7).
    Niblack, W. et al., “The QBIC Project: Querying Images by Content using Color, Textures and Shapes,”Storage and Retrieval for Image and Video Databases, Proc. SPIE 1908, pp. 13–25, 1993.Google Scholar
  8. 8).
    Pentland, A., Picard, R.W., and Sclaroff, S., “Photobook: Content-based Manipulation of Image Databases,”Intl. J. of Computer Vision, 18, 3, pp. 233–254, 1995.CrossRefGoogle Scholar
  9. 9).
    Picard, R.W. “Toward a Visual Thesaurus,”Proc. of Springer-Verlag Workshops in Computing, MIRO’95, Glasgow, Sep. 1995, 1995.Google Scholar
  10. 10).
    Ratan, A.L. and Grimson, W.E.L., “Training Templates for Scene Classification using a Few Examples,”Proc. IEEE Workshop on Content-Based Analysis of Images and Video Libraries, pp. 90–97, 1997.Google Scholar
  11. 11).
    Smith, J.R. and Chang, S.-F., “VisualSEEk: a Fully Automated Content-based Image Query System,”Proc. ACM Multimedia 96, Boston, MA, Nov. 20, 1996.Google Scholar
  12. 12).
    Wood, M.E.J., Campbell, N.W., and Thomas, B.T., “Employing Region Features for Searching an Image Database,”Proc. 1997 British Machine Vision Conference, pp. 620–629, 1997.Google Scholar

Copyright information

© Ohmsha, Ltd. and Springer 2000

Authors and Affiliations

  1. 1.RWCP Information-Base Functions KRDL LabKent Ridge Digital LabsSingapore

Personalised recommendations