A Bayesian framework for video affective representation

@article{Soleymani2009ABF,
  title={A Bayesian framework for video affective representation},
  author={Mohammad Soleymani and Joep J. M. Kierkels and Guillaume Chanel and Thierry Pun},
  journal={2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops},
  year={2009},
  pages={1-7}
}
Emotions that are elicited in response to a video scene contain valuable information for multimedia tagging and indexing. The novelty of this paper is to introduce a Bayesian classification framework for affective video tagging that allows taking contextual information into account. A set of 21 full length movies was first segmented and informative content-based features were extracted from each shot and scene. Shots were then emotionally annotated, providing ground truth affect. The arousal of… CONTINUE READING
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  • The f1 classification measure of 54.9% that was obtained on three emotional classes with a naïve Bayes classifier was improved to 63.4% after utilizing all the priors.

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