Advertisement

A Clustered Retrieval Approach for Categorizing and Annotating Images

  • Lisa Ballesteros
  • Desislava Petkova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)

Abstract

Images are difficult to classify and annotate but the availability of digital image databases creates a constant demand for tools that automatically analyze image content and describe it with either a category or set of words. We develop two cluster-based cross-media relevance models that effectively categorize and annotate images by adapting a cross-lingual retrieval technique to choose the terms most likely associated with the visual features of an image.

Keywords

Language Model Visual Feature Training Image Automatic Image Annotation Annotate Image 
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.
    Duygulu, P., Barnard, K., de Freitas, N., Forsyth, D.: Object recognition as machine translation: Leaning a lexicon for a fixed image vocabulary. In: European Conference on Computer Vision (2002)Google Scholar
  2. 2.
    Deselaers, T., Keysers, D., Ney, H.: FIRE – Flexible Image Retrieval Engine: ImageCLEF 2004 Evaluation. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 688–698. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: A quantitative comparison. In: DAGM Pattern Recognition Symposium (2004)Google Scholar
  4. 4.
    Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Journal of Biological Cybernetics 61, 102–113 (1989)Google Scholar
  5. 5.
    Hearst, M.A., Pedersen, J.O.: Re-examining the cluster hypothesis: Scatter/Gather on retrieval results. ACM SIGIR (1996)Google Scholar
  6. 6.
    Jardine, N., van Rijsbergen, C.J.: The use of hierarchical clustering in information retrieval. Information Storage and Retrieval 7, 217–240 (1971)CrossRefGoogle Scholar
  7. 7.
    Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using Cross-media relevance models. ACM SIGIR (2003)Google Scholar
  8. 8.
    Jeon, J., Manmatha, R.: Using maximum entropy for automatic image annotation. In: Conference on Image and Video Retrieval (2004)Google Scholar
  9. 9.
    Kurland, O., Lee, L.: Corpus structure, language models, and ad hoc information retrieval. ACM SIGIR (2004)Google Scholar
  10. 10.
    Liu, X., Croft, B.: Cluster-based retrieval using language models. ACM SIGIR (2004)Google Scholar
  11. 11.
    Mori, Y., Takanashi, H., Oka, R.: Image-to-word transformation based on dividing and vector quantizing images with words. In: MISRM International Workshop (1999)Google Scholar
  12. 12.
    van Rijsbergen, C.J., Croft, W.B.: Document clustering: An evaluation of some experiments with the Cranfield 1400 collection. Information Processing & Management 11, 171–182 (1975)CrossRefGoogle Scholar
  13. 13.
    Voorhees, E.M.: The cluster hypothesis revisited. ACM SIGIR (1985)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lisa Ballesteros
    • 1
  • Desislava Petkova
    • 1
  1. 1.Mount Holyoke CollegeSouth HadleyUSA

Personalised recommendations