Skip to main content
SpringerLink
Log in

An Image Database System with Support for Traditional Alphanumeric Queries and Content-Based Queries by Example

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Large image databases are commonly employed in applications like criminal records, customs, plant root databases, and voters' registration databases. Efficient and convenient mechanisms for database organization and retrieval are essential. A quick and easy-to-use interface is needed which should also mesh naturally with the overall image management system. In this paper we describe the design and implementation of an integrated image database system. This system offers support for both alphanumeric query, based on alphanumeric data attached to the image file, and content-based query utilizing image examples. Content-based retrieval, specifically Query by Image Example, is made possible by the SHOSLIF approach. Alphanumeric query is implemented by a collection of parsing and query modules. All these are accessible from within a user-friendly GUI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. P. Argentiero, R. Chin, and P. Beaudet, “An automated approach to the design of decision tree classifier,” IEEE Trans. Pattern Anal. Machine Intell., Vol. 4, pp. 51–57, 1983.

    Google Scholar 

  2. J.R. Bach, S. Paul, and R. Jain, “A visual information management system for the interactive retrieval of faces,” IEEE Transactions of Knowledge and Data Engineering, Vol. 5, No. 4, p. 619ff, 1993.

    Google Scholar 

  3. M. Bichsel, “Strategies of robust object recognition for the automatic identification of human faces,” Ph.D. thesis, Eidgenössischen Technischen Hochschule Zürich, Diss. ETH Number 9467, 1991.

  4. S.-K. Chang and T.L. Kunii, “Pictorial data-base systems,” IEEE Computer, pp. 13–21, Nov. 1981.

  5. S.K. Chang, C.W. Yan, D.C. Dimitroff, and T. Arndt, “An intelligent image database system,” IEEE Transactions on Software Engineering, Vol. 14, pp. 681–688, 1988.

    Article  Google Scholar 

  6. P. Eggleston, “Constraint based feature indexing and retrieval for image databases,” in SPIE, 1992, Vol. 1819, pp. 27–39.

    Google Scholar 

  7. O.D. Faugeras and M. Hebert, “The representation, recognition and location of 3-D objects,” Int'l J. Robotics Research, Vol. 5, No. 3, pp. 27–52, 1986.

    Google Scholar 

  8. R.A. Fisher, “The statistical utilization of multiple measurements,” Annals of Eugenics, Vol. 8, pp. 376–386, 1938.

    Google Scholar 

  9. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd edition, Academic Press: New York, 1990.

    Google Scholar 

  10. J.E. Gary and R. Mehrotra, “Shape similarity-based retrieval in image database systems,” in SPIE Image Storage and Retrieval Systems, Vol. 1662, pp. 2–8, 1992.

    Google Scholar 

  11. W.E.L. Grimson, Object Recognition by Computer: The Role of Geometric Constraints, The MIT Press, 1990.

  12. D.P. Huttenlocher and S. Ullman, “Object recognition using alignment,” in Proc. Int'l Conf. Computer Vision, London, England, 1987, pp. 102–111.

  13. K. Ikeuche and T. Kanade, “Automatic generation of object recognition programs,” Proceedings of the IEEE, Vol. 76, No. 8, pp. 1016–1035, 1988.

    Article  Google Scholar 

  14. A.K. Jain and R.C. Dubes, Algorithms for Clustering Data, Prentice-Hall: New Jersey, 1988.

    Google Scholar 

  15. R. Jain and A. Hampapur, “Metadata in video databases,” Sigmod Record: Special Issue on Metadata for Digital Media, ACM: SIGMOD, Vol. 23, p. 27ff, 1994.

    Google Scholar 

  16. I.T. Jolliffe, Principal Component Analysis, Springer-Verlag: New York, 1986.

    Google Scholar 

  17. M. Kirby and L. Sirovich, “Application of the Karhunen-Loève procedure for the characterization of human faces,” IEEE Trans. Pattern Anal. Machine Intell., Vol. 12, pp. 103–108, 1990.

    Article  Google Scholar 

  18. D.J. Kriegman and J. Ponce, “On recognizing and positioning curved 3-D objects from image contours,” IEEE Trans. Pattern Anal. Machine Intell., Vol. 12, No. 12, pp. 1127–1137, 1990.

    Article  Google Scholar 

  19. Y. Lamdan and H.J. Wolfson, “Geometric hashing: A general and efficient model-based recognition scheme,” in Proc. 2nd Int'l Conf. Computer Vision, 1988, pp. 238–249.

  20. M.M. Loève, Probability Theory, Van Nostrand: Princeton, NJ, 1955.

    Google Scholar 

  21. B. Moghaddam and A. Pentland, “Probabilistic visual learning for object detection,” in Proc. International Conference on Computer Vision, 1995, pp. 786–793.

  22. B. Moghaddam and A. Pentland, “Maximum liklihood detection of faces and hands,” in International Workshop on Automatic Face-and Gesture-Recognition, 1995, M. Bichsel (Ed.), pp. 122–128.

  23. H. Murase and S.K. Nayar, “Illumination planning for object recognition in structured environments,” in Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition, Seattle, Washington, June 1994, pp. 31–38.

  24. W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, and C. Faloustos, “The qbic project: Querying images by content using color, texture and shape,” Research Report RJ 9203 (81511), IBM Research Division, Almaden Research Center, 1993.

  25. E. Oomoto and K. Tanaka, “OVID: Design and implementation of a video-object database system,” IEEE Trans. Knowledge and Data Engineering, Vol. 5, p. 629ff, 1993.

    Google Scholar 

  26. A. Pentland, B. Moghaddam, and T. Starner, “View-based and modular eigenspaces for face recognition,” in Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition, Seattle, Washington, June 1994, pp. 84–91.

  27. A. Pentland, R.W. Picard, and S. Scarloff, “Photobook: Tools for content-based manipulation of image databases,” in SPIE Storage and Retrieval Image and Video Databases II, No. 2185, San Jose, 1994.

  28. R.W. Picard and T. Kabir, “Finding similar patterns in large image databases,” in Proc. IEEE ICASSP, Minneapolis, MN, 1993, Vol. 5, pp. 161–164.

    Google Scholar 

  29. F. Stein and G. Medioni, “Efficient two dimensional object recognition,” in 10th International Conference on Pattern Recognition, Atlantic City, 1990.

  30. D.L. Swets, B. Punch, and J.J. Weng,“Genetic algorithms for object recognition in a complex scene,” in Proc. International Conference on Image Processing, Washington, DC, Oct. 1995, pp. 595–598.

  31. D.L. Swets and J.J. Weng, “SHOSLIF-O: SHOSLIF for object recognition and image retrieval (phase II),” Tech. Rep. CPS 95-39, Michigan State University, Department of Computer Science, A714 Wells Hall, East Lansing, Michigan 48824, Oct. 1995.

    Google Scholar 

  32. D.L. Swets and J.J. Weng, “Using discriminant eigenfeatures for image retrieval,” IEEE Trans. Pattern Anal. Machine Intell., 1996, in preparation.

  33. G.Y. Tang, “A management system for an integrated database of pictures and alphanumerical data,” Computer Graphics and Image Processing, Vol. 16, pp. 270–286, 1981.

    Google Scholar 

  34. M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71–86, 1991.

    Google Scholar 

  35. J. Weng, N. Ahuja, and T.S. Huang, “Learning recognition and segmentation using the Cresceptron,” in Proc. International Conference on Computer Vision, Berlin, Germany, May 1993, pp. 121–128.

  36. S.S. Wilks, Mathematical Statistics, Wiley: New York, 1963.

    Google Scholar 

  37. P.H. Winston, Artificial Intelligence, 3rd edition, Addison-Wesley: Reading, MA, p. 404, 1992.

    Google Scholar 

Download references

Authors
  1. Daniel L. Swets
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yogesh Pathak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John J. Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Swets, D.L., Pathak, Y. & Weng, J.J. An Image Database System with Support for Traditional Alphanumeric Queries and Content-Based Queries by Example. Multimedia Tools and Applications 7, 181–212 (1998). https://doi.org/10.1023/A:1009618917283

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1009618917283

Search

Navigation