Affective Classification in Video Based on Semi-supervised Learning

  • Shangfei Wang
  • Huan Lin
  • Yongjie Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6677)


In the previous work of affective video analysis, supervised learning methods are frequently used as classifiers. However, labeling abundant examples is time consuming and even impossible for it needs annotation from human beings. While unlabeled video clips are easy to be obtained and they are adequate. In this paper, we present a semi- supervised approach to recognize emotions from videos. Firstly, visual and audio features are extracted. Then bivariate correlation is used to select sensitive features. After that, low density separation, a semi-supervised learning algorithm, is adopted as the classifier. The comparative experiments on our own constructed database showed that the semi-supervised algorithm performs better than supervised one, illuminating the effectiveness and feasibility of our approach.


Affective classification videos semi-supervised learning 


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  1. 1.
    Nack, F., Dorai, C., Venkatesh, S.: Computational media aesthetics: finding meaning beautiful. IEEE Multimedia 8(4), 10–12 (2001)CrossRefGoogle Scholar
  2. 2.
    Kang, H.-B.: Affective Contents Retrieval from Video with Relevance Feedback. Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access, 243–252 (2003)Google Scholar
  3. 3.
    Hanjalic, A., Xu, L.Q.: Affective video content representation and modeling. IEEE Transactions on Multimedia 7(1), 143–154 (2005)CrossRefGoogle Scholar
  4. 4.
    Xu, M., Jin, J.S., Luo, S., Duan, L.: Hierarchical movie affective content analysis based on arousal and valence features. In: Proceeding of the 16th ACM International Conference on Multimedia, Vancouver, British Columbia, Canada, pp. 677–680 (2008)Google Scholar
  5. 5.
    Arifin, S.: Affective Level Video Segmentation by Utilizing the Pleasure-Arousal-Dominance Information. IEEE Transactions on Multimedia 10(7), 1325–1341 (2008)CrossRefGoogle Scholar
  6. 6.
    Wang, S.F., Wang, X.F.: Emotional semantic detection from multimedia: a brief overview. Kansei Engineering and Soft Computing: Theory and Practice, pp. 126–146. IGI Global, Pennsylvania (2010)Google Scholar
  7. 7.
    Zhu, X.: Semi-Supervised Learning Literature Survey (2008),
  8. 8.
    Schuller, B., Dorfner, J., Rigoll, G.: Determination of Non-Prototypical Valence and Arousal in Popular Music: Features and Performances. EURASIP Journal on Audio, Speech, and Music Processing, Special Issue on Scalable Audio-Content Analysis 2010, 19 pages (2010) Article ID 735854Google Scholar
  9. 9.
    Boersma, P., Weenink, D.: Praat: doing phonetics by computer. Computer Program (2008)Google Scholar
  10. 10.
    Wang, H.L., Cheong, L.-F.: Affective Understanding in Film. IEEE Trans. Circuits System Video Technology 16(6), 689–704 (2006)CrossRefGoogle Scholar
  11. 11.
    Moncrieff, S., Dorai, C., Venkatesh, S.: Affect Computing in Film through Sound Energy Dynamics. In: Proceeding of the Ninth ACM International Conference on Multimedia, Ottawa, Canada, pp. 525–527 (2001)Google Scholar
  12. 12.
    Chapelle, O., Zien, A.: Semi-supervised classification by low density separation. In: Proc. of the Tenth International Workshop on Artificial Intelligence and Statistics (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shangfei Wang
    • 1
  • Huan Lin
    • 1
  • Yongjie Hu
    • 1
  1. 1.Key Lab of Computing and Communicating Software of Anhui Province School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiP.R. China

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