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Predicting Emotional States of Images Using Bayesian Multiple Kernel Learning

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8228)

Abstract

Images usually convey information that can influence people’s emotional states. Such affective information can be used by search engines and social networks for better understanding the user’s preferences. We propose here a novel Bayesian multiple kernel learning method for predicting the emotions evoked by images. The proposed method can make use of different image features simultaneously to obtain a better prediction performance, with the advantage of automatically selecting important features. Specifically, our method has been implemented within a multilabel setup in order to capture the correlations between emotions. Due to its probabilistic nature, our method is also able to produce probabilistic outputs for measuring a distribution of emotional intensities. The experimental results on the International Affective Picture System (IAPS) dataset show that the proposed approach achieves a bette classification performance and provides a more interpretable feature selection capability than the state-of-the-art methods.

Keywords

  • Image emotion
  • low-level image features
  • multiview learning
  • multiple kernel learning
  • variational approximation

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Zhang, H., Gönen, M., Yang, Z., Oja, E. (2013). Predicting Emotional States of Images Using Bayesian Multiple Kernel Learning. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_35

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  • DOI: https://doi.org/10.1007/978-3-642-42051-1_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42050-4

  • Online ISBN: 978-3-642-42051-1

  • eBook Packages: Computer ScienceComputer Science (R0)