Advertisement

An approach to image retrieval for image databases

  • T. Gevers
  • A. W. M. Smeulders
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 720)

Abstract

In this paper, a method is discussed to store and retrieve images efficiently from an image database on the basis of the data structure called E() representation. The E() representation is a spatial knowledge representation preserving the spatial information between objects embedded in symbolic images as an iconic index for the purpose of efficient image retrieval.

The image retrieval method is invariant under, at least, the affine transformation (i.e. translation, rotation and scale) and is able to deal with substantial object occlusion. A metric is defined to express similarity between symbolic images. Initial experiments carried out for two applications show that the image retrieval method is very efficient and robust to similarity retrieval in image databases. Together with the inherent high parallelism, it makes the method a promising image retrieval method.

Keywords

image database image indexing similarity retrieval spatial relations E representation metric spatial query language 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ballard D. H. and Brown C. M., Computer Vision, Prentice-Hall, 1982.Google Scholar
  2. 2.
    Besl, P. J. and Jain, R. C., Three-Dimensional Object Recognition, ACM Computing Surveys, 17(1), 1985, pp. 75–154.CrossRefGoogle Scholar
  3. 3.
    Chang, S.K., Principles of Pictorial Information Systems Design, Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
  4. 4.
    Chang, S. K., Shi, Q. Y. and Yan, C. W., Iconic Indexing by 2-D Strings, IEEE Trans. Pattern Anal. Machine Intell., vol. 9, no. 3, 1987, pp. 413–428.Google Scholar
  5. 5.
    Chang, S. K. and Jungert, E., A Spatial Knowledge Structure for Image Information Systems using Symbolic Projections, Proc. Fall Joint Comp. Conf., Dallas, TX, 1986, pp. 79–86.Google Scholar
  6. 6.
    Chin, R. T. and Dyer, C. R., Model-Based Recognition in Robot Vision, ACM Computing Surveys, 18(1), 1986, pp. 67–108.CrossRefGoogle Scholar
  7. 7.
    Chock, M., Cardenas, A., F. and Klinger, A., Database Structure and Manipulation Capabilities of the Picture Database Management System (PICDMS), IEEE Trans. Pattern Anal. Machine Intell., vol. 6, no. 4, 1984, pp. 484–492.Google Scholar
  8. 8.
    Delone, B. N. and Raikov, D. A., Analytic Geometry, Vol. 2, Moscow, 1949.Google Scholar
  9. 9.
    Egenhofer, M., Spatial Query Languages, PhD Thesis, University of Maine, Orono, 1989.Google Scholar
  10. 10.
    Gevers, T. and Smeulders A. W. M., Enigma: An Image Retrieval System, International Conference on Pattern Recognition, The Hague, The Netherlands, vol. II, 1992, pp. 697–700.Google Scholar
  11. 11.
    Grimson, W.E.L, Object Recognition by Computer, Cambridge, MA: MIT Press, 1990.Google Scholar
  12. 12.
    Guenther, O. and Buchmann A., Research Issues in Spatial Databases, SIGMOD RECORD, vol 19, no. 4, 1990, pp. 61–68.Google Scholar
  13. 13.
    Guttman, A., R-trees: a Dynamic Index Structure for Spatial Searching, Proc. ACM-SIGMOD Int. Conf. Management of Data, June 18–21, 1984, pp. 47–57.Google Scholar
  14. 14.
    Hemant, D. T., Jaffe, C. C. and Duncan, J. S., Arrangements: A Spatial Relation Comparing Part Embeddings, International Conference on Pattern Recognition, The Hague, The Netherlands, vol. 1, 1992, pp. 91–94.Google Scholar
  15. 15.
    Hildreth, E. C. and Ullman, S., The Computational Study of Vision in Foundations of Cognitive Science, M.I.T. Press, 1989.Google Scholar
  16. 16.
    Kate ten, T., Balen van, R., Smeulders, A.W.M., Groen, F.C.A., Boer den, G., SCILAIM: a Multi-level Interactive Image Processing Environment, Pattern Recognition Letters 11, 1990, pp. 429–441.CrossRefGoogle Scholar
  17. 17.
    Klein, F., Elementary Mathematics from an Advanced Standpoint; Geometry, Macmillan, NY, 1925.Google Scholar
  18. 18.
    Lamdan, Y., Schwartz, J. T. and Wolfson, H. J., On Recognition of 3-D Objects from 2-D Images, Proc. of IEEE Int. Conf. on Robotics and Automation, Philadelphia, Pa., 1988, pp. 1407–1413.Google Scholar
  19. 19.
    Lee, S. Y. and Hsu, F. J., Picture Algebra for Spatial Reasoning of Iconic Images represented in 2D C-string, Pattern Recognition Letters, 12, 1991, pp. 425–435.CrossRefGoogle Scholar
  20. 20.
    Samet, H., The Quadtree and Related Data Structures, ACM Computer Surveys, vol. 16. no. 2, 1984, pp. 187–260.CrossRefGoogle Scholar
  21. 21.
    Tamura, H. and Yokota, N., Image Database Systems: A Survey, Pattern Recognition, Vol. 17, no. 1, 1984, pp 29–43.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • T. Gevers
    • 1
  • A. W. M. Smeulders
    • 1
  1. 1.Faculty of Mathematics & Computer ScienceUniversity of AmsterdamSJ AmsterdamThe Netherlands

Personalised recommendations