Video Genre Inference Based on Camera Capturing Models

  • Ping-Hao Wu
  • Sanjay Purushotham
  • C. -C. Jay Kuo
Part of the Studies in Computational Intelligence book series (SCI, volume 287)


On-line video collection is getting larger nowadays. It becomes difficult for users to go through the whole collection to find the video of their interest. To allow efficient browsing, search and retrieval, one intuitive solution is to cluster video clips according to their genres automatically. Then, users’ choices can be narrowed down. Besides on-line video repositories, other applications include managing television broadcasting archives, video conferencing records, etc. The goal of video classification is to automatically place each video title in different categories, such as news, sports, etc. The classification process involves extracting the information from the video clips and classifying them into different classes. In this chapter, we first review related work in this field. Then, two novel features based on the camera shooting process is proposed for video genre classification. These new camera based features exploit the fact that a different genre tends to have different camera effects and user perception. Although a lot of work has been proposed with the consideration of cinematic principles, most extracted features are low-level features without much semantic information.We propose a feature that estimates the number of cameras used in a short time interval. Then, we propose another feature by calculating the distribution of the camera distance, which is approximated by the normalized foreground area of each frame. The block-based motion vector field is adopted to reduce the complexity involved in foreground/background modeling. Preliminary experiment results show that the proposed features capture additional genre-related information so that the video genre can be inferred from the proposed features well.


Motion Vector Foreground Object News Video Video Shot Music Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ping-Hao Wu
    • 1
  • Sanjay Purushotham
    • 1
  • C. -C. Jay Kuo
    • 1
  1. 1.Ming Hsieh Department of Electrical EngineeringUniversity of Southern CaliforniaLos Angeles

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