Skip to main content

Advertisement

A Probabilistic Multimedia Retrieval Model and Its Evaluation

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.

Author information

Correspondence to Thijs Westerveld.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Westerveld, T., de Vries, A.P., van Ballegooij, A. et al. A Probabilistic Multimedia Retrieval Model and Its Evaluation. EURASIP J. Adv. Signal Process. 2003, 985676 (2003). https://doi.org/10.1155/S111086570321101X

Download citation

Keywords

  • multimedia retrieval
  • evaluation
  • probabilistic models
  • Gaussian mixture models
  • language models