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Automatic Determination of Feature Weights for Multi-feature CBIR

  • Peter Wilkins
  • Paul Ferguson
  • Cathal Gurrin
  • Alan F. Smeaton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)

Abstract

Image and video retrieval are both currently dominated by approaches which combine the outputs of several different representations or features. The ways in which the combination can be done is an established research problem in content-based image retrieval (CBIR). These approaches vary from image clustering through to semantic frameworks and mid-level visual features to ultimately determine sets of relative weights for the non-linear combination of features. Simple approaches to determining these weights revolve around executing a standard set of queries with known relevance judgements on some form of training data and is iterative in nature. Whilst successful, this requires both training data and human intervention to derive the optimal weights.

Keywords

Query Image Retrieval Performance Query Time Feature Weight Automatic Determination 
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.

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References

  1. 1.
    Manjunath, B., Salembier, P., Sikora, T. (eds.): Introduction to MPEG-7: Multimedia Content Description Language. Wiley, Chichester (2002)Google Scholar
  2. 2.
    The AceMedia Project, available at http://www.acemedia.org
  3. 3.
    Fox, E.A., Shaw, J.A.: Combination of multiple searches. In: Proceedings of the 2nd Text REtrieval Conference (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Wilkins
    • 1
  • Paul Ferguson
    • 1
  • Cathal Gurrin
    • 1
  • Alan F. Smeaton
    • 1
  1. 1.Centre for Digital Video ProcessingDublin City UniversityDublin 9Ireland

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