Advertisement

Content-based image indexing and retrieval in an image database for technical domains

  • Petra Perner
Indexing and Storage
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1464)

Abstract

The availability of a variety of sophisticated data acquisition instruments has resulted in large repositories of imagery data in different applications like non-destructive testing, technical drawing, medicine, museums and so one. Effective extraction of visual features and contents is needed to provide meaningful index of and access to visual data. In the paper, we proposed an image database architecture, which can be used for most industrial problems. The image database is able to handle structural representations of images. Indexing is possible object based, spatial relation based, and by a combination of both. The query can be a textual query or an image content-based query. We describe how the image query is processed, how similarity based retrieval is performed over images, and how the image database is organized. Results are presented based on an application of ultra sonic images from non-destructive testing.

Keywords

Image Database Query-by-Image-Content Structural Similarity Measure Indexing Learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S.-F. Chang,“Content-Based Indexing and Retrieval of Visual Informations,” IEEE Signal Processing Magazine (July 1997), vol. 14, no.4, p. 45–48.Google Scholar
  2. 2.
    C., Meghini, “An image retrieval model based on classical logic”, SIGIR Forum (1995) spec. Issue, p. 300–308Google Scholar
  3. 3.
    Th. Whalen, E.S. Lee, and F. Safaycni, “The retrieval of images for image databases: trademarks,” Behaviour and information technology, 1995, vol. 14, No. 1, 3–13.Google Scholar
  4. 4.
    A.R. Rao, A Taxonomy for Texture Description and Identification, Springer Verlag 1990.Google Scholar
  5. 5.
    R. Mehrotra, “Integrated Image Information Managment: Renal Issues,”. Proc of the SPIE 1995, vol. 2488, 164–177.Google Scholar
  6. 6.
    J. Hildebrandt, C. Irving, and K. Tang,“An Implementation of Image Database Systems using differing indexing methods,” Proc. of the SPIE (1995), vol. 2606, p. 246–257.Google Scholar
  7. 7.
    R. Sarnadani, C. Han, and L.K. Katragadda, “Content based event selection from satellite images of the aurora,” Proc. of SPIE (1993), vol. 1908, p. 50–59.Google Scholar
  8. 8.
    Q.-L. Zhang, S.-K. Chang, and S. S.-T. Yau, ”A unified approach to iconic indexing, retrieval, and maintenance of spatial relationships in image databases,” Journal of Visual Communiction and Image Representation (Dec. 1996), vol. 7, no.4, p. 307–24.Google Scholar
  9. 9.
    D. Lee, R. Barber, and Wayne Niblack, “Indexing for Complex Queries on a Query-By-Content Image Database,” In Proc. of the ICPR'94, Jerusalem Oct. 9–13 1994, Israel, vol. 1, p. 142–146.Google Scholar
  10. 10.
    D. Papadias and T. Sellis, “A Pictorial Query-By-Example Language,” Journal of Visual Languages and Computing (1995) 6, p. 53–72.Google Scholar
  11. 11.
    M.S. Lew, D.P. Huijsmans, and D. Denteneer, “Optimal keys for image database,” In Proc. of the 9th ICIAP'97, A. Del Bimbo (Eds.), Springer Verlag 1997, p. 148–55.Google Scholar
  12. 12.
    F.R. Johannesen, S. Raaschou, O.V. Larsen, and P. kirgensen,“ Using Weighted Minutiae for Fingerprint Identification,” In Proc: P. Perner, P. Wang, and A. Rosenfeld (Eds.), Advances in Structural and Syntactical. Pattern Recognition, Springer Verlag 1996, pp. 289–299Google Scholar
  13. 13.
    H. Bunke, K. Wakimoto, S. Tanaka, and A. Maeda, “A similarity retrieval method of drawings based graph representation,” Systems and Computer in Japan, Oct. 1995, vol. 26, no. 11, p. 100–109.Google Scholar
  14. 14.
    G. Vosselman, Relational Matching, Lecture Notes in Computer Sciences, Springer Verlag 1992Google Scholar
  15. 15.
    D. Lee, R. Barber, W, Niblack, M. Flickner, J. Hafner, and D. Petkovic, “Query by Image Content using multiple objects and multiple features: User Interface Issues. In ICIP, Austin, TX, 1994Google Scholar
  16. 16.
    P, Zamperoni,“Feature Extraction,” In: H. Maitre and J. Zinn-Justin, Progress in Picture Processing, Elsevier Science, 1996, pp. 123–184.Google Scholar
  17. 17.
    B. B. Mandelbrot, The Fractal Geometry of Nature, W.H. Freeman and Company 1977Google Scholar
  18. 18.
    D. Hernandez,“Relative Representation of Spatial Knowledge: The 2-D Case,” Report FKI-135-90, Aug. 1990, TU Muenchen.Google Scholar
  19. 19.
    JR. Schirra, “A Contribution to Reference Semantics of Spatial Prepositions: the Visualization and its Solution in VITRA,” SFB 314, KI-Wissensbasierte Systeme, Dec. 1990.Google Scholar
  20. 20.
    M.I. Schlesinger, “Mathematical Tools of Picture Processing”, (in Russian), Naukowa Dumka, Kiew 1989.Google Scholar
  21. 21.
    F. Harary, “Graph Theory,” Addison-Wesley 1972.Google Scholar
  22. 22.
    P. Perner and W. Patzold,“A Incremental Leming System for Interpretation of Images,” In Proc. of SSPR: D. Dori and A. Brackstein (Eds.) Shape, Structure and Pattern Recognition, World Scientific Publishing Co., pp. 311–323Google Scholar
  23. 23.
    P. Pemer,“Ultra Sonic Image Interpretation for Non-Destructive Testing,” In Proc. MVA'96, pp. 552–554.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Petra Perner
    • 1
  1. 1.Institute of Computer Vision and Applied Computer SciencesLeipzigGermany

Personalised recommendations