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Proceedings Paper

Combining motion understanding and keyframe image analysis for broadcast video information extraction
Author(s): Ming-yu Chen; Huan Li; Alexander Hauptmann
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Paper Abstract

We describe a robust new approach to extract semantic concept information based on explicitly encoding static image appearance features together with motion information. For high-level semantic concept identification detection in broadcast video, we trained multi-modality classifiers which combine the traditional static image features and a new motion feature analysis method (MoSIFT). The experimental result show that the combined features have solid performance for detecting a variety of motion related concepts and provide a large improvement over static image analysis features in video.

Paper Details

Date Published: 15 April 2010
PDF: 9 pages
Proc. SPIE 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV, 77040H (15 April 2010); doi: 10.1117/12.853465
Show Author Affiliations
Ming-yu Chen, Carnegie Mellon Univ. (United States)
Huan Li, BeiHang Univ. (China)
Alexander Hauptmann, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 7704:
Evolutionary and Bio-Inspired Computation: Theory and Applications IV
Teresa H. O'Donnell; Misty Blowers; Kevin L. Priddy, Editor(s)

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