Introduction to algorithms
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
International Journal of Computer Vision
The trecvid 2007 BBC rushes summarization evaluation pilot
Proceedings of the international workshop on TRECVID video summarization
Video rushes summarization by adaptive acceleration and stacking of shots
Proceedings of the international workshop on TRECVID video summarization
National institute of informatics, japan at TRECVID 2007: BBC rushes summarization
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
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Rushes video summarization and evaluation
Multimedia Tools and Applications
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The film making industry, together with ordinary-home users, are producing a record number of multimedia videos, generating a great demand for new methods to explore the content available in these videos. Here we focus in one methods for automatic rushes video summarization. Rushes consist of unedited material generated during the recording of a video film, and have characteristics not always found in standard videos: a high number of repetitions and a great number of the so called junk shots. To solve this problem, we propose an approach based on spatial and spatial-temporal features represented by a bags of visual features. This representation is robust to a series of transformations in image and occlusion. The task is modeled as an optimization problem, and a strategy inspired by the multiview learning technique is applied. Results on the BBC Rushes database were compared with the three best methods submitted to the TRECVID 2007, and showed the methodology to be promising for dynamic rushes video summarization.