Combining motion understanding and keyframe image analysis for broadcast video information extraction

Ming-yu Chen; Huan Li; Alexander Hauptmann

doi:10.1117/12.853465

15 April 2010 Combining motion understanding and keyframe image analysis for broadcast video information extraction

Ming-yu Chen, Huan Li, Alexander Hauptmann

Proceedings Volume 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV; 77040H (2010) https://doi.org/10.1117/12.853465
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

Ming-yu Chen, Huan Li, and Alexander Hauptmann "Combining motion understanding and keyframe image analysis for broadcast video information extraction", Proc. SPIE 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV, 77040H (15 April 2010); https://doi.org/10.1117/12.853465

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