Content-based mood classification for photos and music: a generic multi-modal classification framework and evaluation approach

  • Authors:

  • Peter Dunker;Stefanie Nowak;André Begau;Cornelia Lanz

  • Affiliations:

  • Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany;Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany;Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany;Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany

  • Venue:
  • MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
  • Year:
  • 2008

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Abstract

Mood or emotion information are often used search terms or navigation properties within multimedia archives, retrieval systems or multimedia players. Most of these applications engage end-users or experts to tag multimedia objects with mood annotations. Within the scientific community different approaches for content-based music, photo or multimodal mood classification can be found with a wide range of used mood definitions or models and completely different test suites. The purpose of this paper is to review common mood models in order to assess their flexibility, to present a generic multi-modal mood classification framework which uses various audio-visual features and multiple classifiers and to present a novel music and photo mood classification reference set for evaluation. The classification framework is the basis for different applications e.g. automatic media tagging or music slideshow players. The novel reference set can be used for comparison of different algorithms from various research groups. Finally, the results of the introduced framework are presented, discussed and conclusions for future steps are drawn.