Towards robust features for classifying audio in the CueVideo system
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Hierarchical hidden markov model for rushes structuring and indexing
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Two-stage hierarchical video summary extraction to match low-level user browsing preferences
IEEE Transactions on Multimedia
Automated video program summarization using speech transcripts
IEEE Transactions on Multimedia
Moving-Object Detection, Association, and Selection in Home Videos
IEEE Transactions on Multimedia
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
Video partitioning by temporal slice coherency
IEEE Transactions on Circuits and Systems for Video Technology
Dimensionality reduction for heterogeneous dataset in rushes editing
Pattern Recognition
Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Regim, research group on intelligent machines, tunisia, at TRECVID 2008, BBC rushes summarization
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
THU-intel at rushes summarization of TRECVID 2008
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Automatically estimating number of scenes for rushes summarization
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Improving retake detection by adding motion feature
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
| Hi-index | 0.00 |
This paper explores a variety of visual and audio analysis techniques in selecting the most representative video clips for rushes summarization at TRECVID 2007. These techniques include object detection, camera motion estimation, keypoint matching and tracking, audio classification and speech recognition. Our system is composed of two major steps. First, based on video structuring, we filter undesirable shots and minimize theinter-shot redundancy by repetitive shot detection. Second, a representability measure is proposed to model the presence of objects and four audio-visual events: motion activity of objects, camera motion, scene changes,and speech content, in a video clip. The video clips with the highest representability scores are selected for summarization. The evaluation at TRECVID shows that our experimental results are highly encouraging, where we rank first in EA (easy to understand), second in RE (little redundancy) and third in IN (inclusion of objects and events).