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A Bayesian Framework for Semantic Content Characterization

Nuno Vasconcelos, Andrew Lippman
CVPR 1998 pp. 566-571
doi:10.1109/CVPR.1998.698662 /cvpr/1998/vasconcelos1998cvpr-bayesian/

Abstract

Current systems for content filtering, browsing, and retrieval rely on low-level image descriptors which are unintuitive for most users. In this paper, we propose an alternative framework that exploits the structured nature of most content sources to achieve semantic content characterization, and lead to much more meaningful user interaction. Computationally, this framework is based on the principles of Bayesian inference and can be implemented efficiently with Bayesian networks. As an illustration of its potential we apply it to the domain of movie databases.

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Cite

Text

Vasconcelos and Lippman. "A Bayesian Framework for Semantic Content Characterization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998. doi:10.1109/CVPR.1998.698662

Markdown

[Vasconcelos and Lippman. "A Bayesian Framework for Semantic Content Characterization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998.](https://mlanthology.org/cvpr/1998/vasconcelos1998cvpr-bayesian/) doi:10.1109/CVPR.1998.698662

BibTeX

@inproceedings{vasconcelos1998cvpr-bayesian,
  title     = {{A Bayesian Framework for Semantic Content Characterization}},
  author    = {Vasconcelos, Nuno and Lippman, Andrew},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1998},
  pages     = {566-571},
  doi       = {10.1109/CVPR.1998.698662},
  url       = {https://mlanthology.org/cvpr/1998/vasconcelos1998cvpr-bayesian/}
}