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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)

Abstract

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.

Keywords

Affective classification videos semi-supervised learning 

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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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