Video Manga: generating semantically meaningful video summaries
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The trecvid 2007 BBC rushes summarization evaluation pilot
Proceedings of the international workshop on TRECVID video summarization
Clever clustering vs. simple speed-up for summarizing rushes
Proceedings of the international workshop on TRECVID video summarization
Rushes video summarization by object and event understanding
Proceedings of the international workshop on TRECVID video summarization
Generating comprehensible summaries of rushes sequences based on robust feature matching
Proceedings of the international workshop on TRECVID video summarization
Skimming rushes video using retake detection
Proceedings of the international workshop on TRECVID video summarization
NTU TRECVID-2007 fast rushes summarization system
Proceedings of the international workshop on TRECVID video summarization
THU-ICRC at rush summarization of TRECVID 2007
Proceedings of the international workshop on TRECVID video summarization
Feature fusion and redundancy pruning for rush video summarization
Proceedings of the international workshop on TRECVID video summarization
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Rate-distortion optimal video summary generation
IEEE Transactions on Image Processing
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
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Video summary is an active research field to help users to grasp a whole video's content for efficient browsing and editing. In this paper, we describe our THU-Intel rushes summarization system in TRECVID2008. In our approach, we first extract low-level audiovisual features and parse the video into shots, sub-shots and 1-second video clips. Then we remove junk video clips with color-bar, near uniform-color and clapboard frames etc. To select video clips with main objects and events, we evaluate each clip's representative score by multimodal features of color, edge, motion, and audio etc. Finally, we construct the rushes video summary by iteratively selecting the most representative video clips and removing similar ones. Extensive experiments are carried out on 40 testing rushes videos. Good results demonstrate the effectiveness of the proposed method.