Advertisement

Image Database Assisted Classification

  • Simone Santini
  • Marcel Worring
  • Edd Hunter
  • Valentina Kouznetsova
  • Michael Goldbaum
  • Adam Hoover
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1614)

Abstract

Image similarity can be defined in a number of different semantic contexts. At the lowest common denominator, images may be classified as similar according to geometric properties, such as color and shape distributions. At the mid-level, a deeper image similarity may be defined according to semantic properties, such as scene content or description. We propose an even higher level of image similarity, in which domain knowledge is used to reason about semantic properties, and similarity is based on the results of reasoning.

At this level, images with only slightly different (or similar) semantic descriptions may be classified as radically different (or similar), based upon the execution of the domain knowledge. For demonstration, we show experiments performed on a small database of 300 images of the retina, classified according to fourteen diagnoses.

Keywords

Bayesian Network Domain Knowledge Image Database Image Similarity Marginal Probability 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: the QBIC system. IEEE Computer, 28(9), 1995.Google Scholar
  2. 2.
    A. Gupta and R. Jain. Visual information retrieval. Communications of the ACM, 40(5):70–79, 1997.CrossRefGoogle Scholar
  3. 3.
    A. Hoover, M. Goldbaum, A. Taylor, J. Boyd, T. Nelson, S. Burgess, G. Celikkol, and R. Jain. Schema for standardized description of digital ocular fundus image contents. In ARVO Investigative Ophthalmology and Visual Science, Fort Lauderdale, FL, 1998. Abstract.Google Scholar
  4. 4.
    F. Jensen. Hugin api reference manual, version 3.1, hugin expert a/s, 1997.Google Scholar
  5. 5.
    V.E. Ogle and M. Stonebraker. Chabot: retrieval from a relational database of images. IEEE Computer, 28(9), 1995.Google Scholar
  6. 6.
    G.W.A.M. van der Heijden and M. Worring. Domain concept to feature mapping for a plant variety image database. In A.W.M. Smeulders and R. Jain, editors, Image Databases and Multimedia Search, volume 8 of Series on software engingeering and knowledge engineering, pages 301–308. World Scientific, 1997.Google Scholar
  7. 7.
    N. Vasconcelos and A. Lippman. A Bayesian framework for semantic content characterization. In Proceedings of the CVPR, pages 566–571, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Simone Santini
    • 1
  • Marcel Worring
    • 2
  • Edd Hunter
    • 1
  • Valentina Kouznetsova
    • 1
  • Michael Goldbaum
    • 3
  • Adam Hoover
    • 1
  1. 1.Visual Computing LabUniversity of California San DiegoSan Diego
  2. 2.Intelligent Sensory Information SystemsUniversity of AmsterdamAmsterdam
  3. 3.Department of OphthalmologyUniversity of California San DiegoSan Diego

Personalised recommendations