Hierarchical affective content analysis in arousal and valence dimensions

  • Authors:

  • Min Xu;Changsheng Xu;Xiangjian He;Jesse S. Jin;Suhuai Luo;Yong Rui

  • Affiliations:

  • School of Computing and Communications, University of Technology Sydney, Australia and National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China;National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China;School of Computing and Communications, University of Technology Sydney, Australia;Faculty of Science and I.T., University of Newcastle, Australia;Faculty of Science and I.T., University of Newcastle, Australia;Microsoft Research, China

  • Venue:
  • Signal Processing
  • Year:
  • 2013

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Abstract

Different from the existing work focusing on emotion type detection, the proposed approach in this paper provides flexibility for users to pick up their favorite affective content by choosing either emotion intensity levels or emotion types. Specifically, we propose a hierarchical structure for movie emotions and analyze emotion intensity and emotion type by using arousal and valence related features hierarchically. Firstly, three emotion intensity levels are detected by using fuzzy c-mean clustering on arousal features. Fuzzy clustering provides a mathematical model to represent vagueness, which is close to human perception. Then, valence related features are used to detect five emotion types. Considering video is continuous time series data and the occurrence of a certain emotion is affected by recent emotional history, conditional random fields (CRFs) are used to capture the context information. Outperforming Hidden Markov Model, CRF relaxes the independence assumption for states required by HMM and avoids bias problem. Experimental results show that CRF-based hierarchical method outperforms the one-step method on emotion type detection. User study shows that majority of the viewers prefer to have option of accessing movie content by emotion intensity levels. Majority of the users are satisfied with the proposed emotion detection.