Advertisement

Clustering for Photo Retrieval at Image CLEF 2008

  • Diana Inkpen
  • Marc Stogaitis
  • François DeGuire
  • Muath Alzghool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

This paper presents the first participation of the University of Ottawa group in the Photo Retrieval task at Image CLEF 2008. Our system uses Lucene for text indexing and LIRE for image indexing. We experiment with several clustering methods in order to retrieve images from diverse clusters. The clustering methods are: k-means clustering, hierarchical clustering, and our own method based on WordNet. We present results for thirteen runs, in order to compare retrieval based on text description, to image-only retrieval, and to merged retrieval, and to compare results for the different clustering methods.

Keywords

Information retrieval image retrieval photographs text retrieval  k-means clustering agglomerative clustering WordNet-based clustering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fellbaum, C. (ed.): WordNet, An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  2. 2.
    Lee, J.H.: Combining multiple evidence from different properties of weighting schemes. In: Proceedings of the Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, Washington, United States. ACM, New York (1995)Google Scholar
  3. 3.
    Shaw, J.A., Fox, E.A.: Combination of Multiple Searches. National Institute of Standards and Technology Special Publication (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Diana Inkpen
    • 1
  • Marc Stogaitis
    • 1
  • François DeGuire
    • 1
  • Muath Alzghool
    • 1
  1. 1.School of Information Technology and EngineeringUniversity of OttawaCanada

Personalised recommendations