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Video summarization from spatio-temporal features

Published: 31 October 2008 Publication History

Abstract

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.

References

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D. Wedge, D. Huynh, P. Kovesi, Using Space-Time Interest Points for Video Sequence Synchronization, IAPR Conference on Machine Vision Applications, pages 190--194, Tokyo, Japan, 2007.
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A. G. Money, H. Agius, Video summarisation: A conceptual framework and survey of the state of the art, Journal of Visual Communication and Image Representation, 19(2), pages 121--143, 2008.
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  • (2022)SUM-GAN-GEA: Video Summarization Using GAN with Gaussian Distribution and External AttentionElectronics10.3390/electronics1121352311:21(3523)Online publication date: 29-Oct-2022
  • (2022)Leveraging semantic saliency maps for query-specific video summarizationMultimedia Tools and Applications10.1007/s11042-022-12442-w81:12(17457-17482)Online publication date: 7-Mar-2022
  • (2022)Video summarization with u-shaped transformerApplied Intelligence10.1007/s10489-022-03451-152:15(17864-17880)Online publication date: 6-Apr-2022
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cover image ACM Conferences
TVS '08: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
October 2008
156 pages
ISBN:9781605583099
DOI:10.1145/1463563
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 October 2008

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Author Tags

  1. hessian-laplace
  2. spatio-temporal features
  3. video abstracts

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MM08
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MM08: ACM Multimedia Conference 2008
October 31, 2008
British Columbia, Vancouver, Canada

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Cited By

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  • (2022)SUM-GAN-GEA: Video Summarization Using GAN with Gaussian Distribution and External AttentionElectronics10.3390/electronics1121352311:21(3523)Online publication date: 29-Oct-2022
  • (2022)Leveraging semantic saliency maps for query-specific video summarizationMultimedia Tools and Applications10.1007/s11042-022-12442-w81:12(17457-17482)Online publication date: 7-Mar-2022
  • (2022)Video summarization with u-shaped transformerApplied Intelligence10.1007/s10489-022-03451-152:15(17864-17880)Online publication date: 6-Apr-2022
  • (2021)Text Synopsis Generation for Egocentric Videos2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412111(4252-4259)Online publication date: 10-Jan-2021
  • (2021)Learning Triadic Belief Dynamics in Nonverbal Communication from Videos2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00723(7308-7317)Online publication date: Jun-2021
  • (2019)Video SkimmingACM Computing Surveys10.1145/334771252:5(1-38)Online publication date: 13-Sep-2019
  • (2019)An overview of machine learning techniques applicable for summarisation of characters in videos2019 International Conference on Intelligent Computing and Control Systems (ICCS)10.1109/ICCS45141.2019.9065654(178-181)Online publication date: May-2019
  • (2019)Video Summarization by Learning From Unpaired Data2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2019.00809(7894-7903)Online publication date: Jun-2019
  • (2019)Rethinking the Evaluation of Video Summaries2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2019.00778(7588-7596)Online publication date: Jun-2019
  • (2019)Spatiotemporal Modeling for Video Summarization Using Convolutional Recurrent Neural NetworkIEEE Access10.1109/ACCESS.2019.29169897(64676-64685)Online publication date: 2019
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