Open Access

A Probabilistic Multimedia Retrieval Model and Its Evaluation

  • Thijs Westerveld1Email author,
  • Arjen P. de Vries1,
  • Alex van Ballegooij1,
  • Franciska de Jong2 and
  • Djoerd Hiemstra2
EURASIP Journal on Advances in Signal Processing20032003:985676

DOI: 10.1155/S111086570321101X

Received: 21 March 2002

Published: 25 February 2003

Abstract

We present a probabilistic model for the retrieval of multimodal documents. The model is based on Bayesian decision theory and combines models for text-based search with models for visual search. The textual model is based on the language modelling approach to text retrieval, and the visual information is modelled as a mixture of Gaussian densities. Both models have proved successful on various standard retrieval tasks. We evaluate the multimodal model on the search task of TREC′s video track. We found that the disclosure of video material based on visual information only is still too difficult. Even with purely visual information needs, text-based retrieval still outperforms visual approaches. The probabilistic model is useful for text, visual, and multimedia retrieval. Unfortunately, simplifying assumptions that reduce its computational complexity degrade retrieval effectiveness. Regarding the question whether the model can effectively combine information from different modalities, we conclude that whenever both modalities yield reasonable scores, a combined run outperforms the individual runs.

Keywords

multimedia retrieval evaluation probabilistic models Gaussian mixture models language models

Authors’ Affiliations

(1)
National Research Institute for Mathematics and Computer Science (CWI)
(2)
University of Twente

Copyright

© Copyright © 2003 Hindawi Publishing Corporation 2003