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Multimodal Semantic Analysis and Annotation for Basketball Video

  • Song Liu
  • Min Xu
  • Haoran Yi
  • Liang-Tien Chia
  • Deepu Rajan
Open Access
Research Article
Part of the following topical collections:
  1. Information Mining from Multimedia Databases

Abstract

This paper presents a new multiple-modality method for extracting semantic information from basketball video. The visual, motion, and audio information are extracted from video to first generate some low-level video segmentation and classification. Domain knowledge is further exploited for detecting interesting events in the basketball video. For video, both visual and motion prediction information are utilized for shot and scene boundary detection algorithm; this will be followed by scene classification. For audio, audio keysounds are sets of specific audio sounds related to semantic events and a classification method based on hidden Markov model (HMM) is used for audio keysound identification. Subsequently, by analyzing the multimodal information, the positions of potential semantic events, such as "foul" and "shot at the basket," are located with additional domain knowledge. Finally, a video annotation is generated according to MPEG-7 multimedia description schemes (MDSs). Experimental results demonstrate the effectiveness of the proposed method.

Keywords

Hide Markov Model Domain Knowledge Boundary Detection Motion Prediction Semantic Event 
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

© Liu et al. 2006

Authors and Affiliations

  • Song Liu
    • 1
  • Min Xu
    • 1
  • Haoran Yi
    • 1
  • Liang-Tien Chia
    • 1
  • Deepu Rajan
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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