Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Video summarisation: A conceptual framework and survey of the state of the art
Journal of Visual Communication and Image Representation
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
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In this paper we present a video summarization method based on the study of spatio-temporal activity within the video. The visual activity is estimated by measuring the number of interest points, jointly obtained in the spatial and temporal domains. The proposed approach is composed of five steps. First, image features are collected using the spatio-temporal Hessian matrix. Then, these features are processed to retrieve the candidate video segments for the summary (denoted clips). Further on, two specific steps are designed to first detect the redundant clips, and second to eliminate the clapperboard images. The final step consists in the construction of the final summary which is performed by retaining the clips showing the highest level of activity. The proposed approach was tested on the BBC Rushes Summarization task within the TRECVID 2008 campaign.