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Published Articles >> Table of Contents >> Abstract
Eighth IEEE International Symposium on Multimedia (ISM'06)
pp. 193-200
Exciting Event Detection Using Multi-level Multimodal Descriptors and Data Classification
Shu-Ching Chen, Florida International University, USA
Min Chen, Florida International University, USA
Chengcui Zhang, University of Alabama at Birmingham, USA
Mei-Ling Shyu, University of Miami, USA
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISM.2006.73
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| Abstract |
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Event detection is of great importance in high-level
semantic indexing and selective browsing of video
clips. However, the use of low-level visual-audio
feature descriptors alone generally fails to yield
satisfactory results in event identification due to the
semantic gap issue. In this paper, we propose an
advanced approach for exciting event detection in
soccer video with the aid of multi-level descriptors
and classification algorithm. Specifically, a set of
algorithms are developed for efficient extraction of
meaningful mid-level descriptors to bridge the
semantic gap and to facilitate the comprehensive video
content analysis. The data classification algorithm is
then performed upon the combination of multimodal
mid-level descriptors and low-level feature descriptors
for event detection. The effectiveness and efficiency of
the proposed framework are demonstrated over a
large collection of soccer video data with different
styles produced by different broadcasters.
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 Additional Information
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Citation:
Shu-Ching Chen, Min Chen, Chengcui Zhang, Mei-Ling Shyu,
"Exciting Event Detection Using Multi-level Multimodal Descriptors and Data Classification,"
ism,
pp. 193-200,
Eighth IEEE International Symposium on Multimedia (ISM'06),
2006
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