An efficient automatic video shot size annotation scheme

  • Authors:

  • Meng Wang;Xian-Sheng Hua;Yan Song;Wei Lai;Li-Rong Dai;Ren-Hua Wang

  • Affiliations:

  • Department of EEIS, University of Sci&Tech of China, Hefei, Anhui, China;Microsoft Research Asia, Beijing, China;Department of EEIS, University of Sci&Tech of China, Hefei, Anhui, China;Microsoft Research Asia, Beijing, China;Department of EEIS, University of Sci&Tech of China, Hefei, Anhui, China;Department of EEIS, University of Sci&Tech of China, Hefei, Anhui, China

  • Venue:
  • MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
  • Year:
  • 2007

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Abstract

This paper presents an efficient learning scheme for automatic annotation of video shot size. Instead of existing methods that applied in sports videos using domain knowledge, we are aiming at a general approach to deal with more video genres, by using a more general low- and mid- level feature set. Support Vector Machine (SVM) is adopted in the classification task, and an efficient co-training scheme is used to explore the information embedded in unlabeled data based on two complementary feature sets. Moreover, the subjectivity-consistent costs for different mis-classifications are introduced to make the final decisions by a cost minimization criterion. Experimental results indicate the effectiveness and efficiency of the proposed scheme for shot size annotation.