Predicting emotional states of images using Bayesian multiple kernel learning

He Zhang, Mehmet Gönen, Zhirong Yang, Erkki Oja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Number of pages9
EditionPART 3
StatePublished - 2013
Externally publishedYes
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: Nov 3 2013Nov 7 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8228 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other20th International Conference on Neural Information Processing, ICONIP 2013
Country/TerritoryKorea, Republic of


  • Image emotion
  • Low-level image features
  • Multiple kernel learning
  • Multiview learning
  • Variational approximation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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