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Automatic evaluation of video summaries

Published: 06 August 2012 Publication History

Abstract

This article describes a method for the automatic evaluation of video summaries based on the training of individual predictors for different quality measures from the TRECVid 2008 BBC Rushes Summarization Task. The obtained results demonstrate that, with a large set of evaluation data, it is possible to train fully automatic evaluation systems based on visual features automatically extracted from the summaries. The proposed approach will enable faster and easier estimation of the results of newly developed abstraction algorithms and the study of which summary characteristics influence their perceived quality.

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Cited By

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  • (2024)Multi-Reference Evaluation of Dynamic Video Summaries Using Granule-Aware F-MeasureIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33698558:4(3040-3054)Online publication date: Aug-2024
  • (2021)Video summary generation by visual shielding compressed sensing coding and double-layer affinity propagationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2021.103321(103321)Online publication date: Sep-2021
  • (2016)Scalable storyboards in handheld devicesMultimedia Tools and Applications10.1007/s11042-014-2421-475:20(12597-12625)Online publication date: 1-Oct-2016
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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 8, Issue 3
July 2012
143 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/2240136
Issue’s Table of Contents
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 August 2012
Accepted: 01 April 2011
Revised: 01 August 2010
Received: 01 April 2010
Published in TOMM Volume 8, Issue 3

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Author Tags

  1. Automatic evaluation
  2. skimming
  3. summarization
  4. summarization algorithms evaluation

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  • Research-article
  • Research
  • Refereed

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  • Spanish Government

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Cited By

View all
  • (2024)Multi-Reference Evaluation of Dynamic Video Summaries Using Granule-Aware F-MeasureIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33698558:4(3040-3054)Online publication date: Aug-2024
  • (2021)Video summary generation by visual shielding compressed sensing coding and double-layer affinity propagationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2021.103321(103321)Online publication date: Sep-2021
  • (2016)Scalable storyboards in handheld devicesMultimedia Tools and Applications10.1007/s11042-014-2421-475:20(12597-12625)Online publication date: 1-Oct-2016
  • (2015)On-Line Multi-View Video Summarization for Wireless Video Sensor NetworkIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2014.23319169:1(165-179)Online publication date: Feb-2015
  • (2014)Diverse sequential subset selection for supervised video summarizationProceedings of the 28th International Conference on Neural Information Processing Systems - Volume 210.5555/2969033.2969058(2069-2077)Online publication date: 8-Dec-2014
  • (2013)Large-Scale Video Summarization Using Web-Image PriorsProceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2013.348(2698-2705)Online publication date: 23-Jun-2013

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