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Histogram families for color-based retrieval in image databases

  • Carlo Colombo
  • Alessandro Rizzi
  • Ivan Genovesi
Poster Session C: Compression, Hardware & Software, Image Database, Neural Networks, Object Recognition & Reconstruction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

A system for image representation and retrieval in a pictorial database using color distribution features is presented. Images are internally described and matched one against the other by means of a set of color histograms taking into account the local characteristics of chromatic image structure. A graphic environment allows the user to compose interactively pictorial queries by both color sketch and image examples. It is also possible to the user to exploit the history of previous queries to affect current system output. Experimental evidence relating system performance to human expectation is presented and discussed.

Keywords

System Output Query Image Pictorial Content Previous Query Global Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    V.N. Gudivada and V.V. Raghavan, eds. Content-Based Image Retrieval Systems. IEEE Computer 28(9), September 1995. (Special Issue.)Google Scholar
  2. 2.
    R.W. Picard and A.P. Pentland, eds. Digital Libraries: Representation and Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), August 1996. (Special Issue.)Google Scholar
  3. 3.
    V.E. Ogle and M. Stonebraker. CHABOT: Retrieval from a Relational Database of Images. IEEE Computer 28(9), 1995.Google Scholar
  4. 4.
    M. Swain and D. Ballard. Color Indexing. International Journal of Computer Vision 7(11), 1991.Google Scholar
  5. 5.
    K. Hirata and T. Kato. Query by Visual Example — Content-Based Image Retrieval. In Proc. EDBT'92, pages 56–71, Springer LNCS 1992.Google Scholar
  6. 6.
    W. Niblack et al. The QBIC Project: Querying Images by Content using Color, Texture and Shape. Research Report 9203, IBM Research Division, Almaden Research Center, 1993.Google Scholar
  7. 7.
    A. Del Bimbo and P. Pala. Visual Image Retrieval by Elastic Matching of User Sketches. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2), 1997.Google Scholar
  8. 8.
    S.-K. Chang and E. Jungert. Pictorial Data Management based upon the Theory of Symbolic Projections. Journal of Visual Languages and Computing 2(2), 1991.Google Scholar
  9. 9.
    S. Santini and R. Jain. The Graphical Specification of Similarity Queries. Journal of Visual Languages and Computing 4(5), 1996.Google Scholar
  10. 10.
    I. Genovesi. Ricerca Interattiva per Distribuzioni di Colore in Database di Immagini. Master's Thesis, Polytechnic of Milan, April 1997. (In Italian.)Google Scholar
  11. 11.
    D. Ballard and C. Brown. Computer Vision. Prentice-Hall, Engelwood Cliffs NJ, 1982.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Carlo Colombo
    • 1
  • Alessandro Rizzi
    • 1
  • Ivan Genovesi
    • 1
  1. 1.Dipartimento di Elettronica per l'AutomazioneUniversitá di BresciaBresciaItaly

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