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The trecvid 2008 BBC rushes summarization evaluation

Authors:

Alan F. Smeaton,

George AwadAuthors Info & Claims

TVS '08: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop

Pages 1 - 20

https://doi.org/10.1145/1463563.1463564

Published: 31 October 2008 Publication History

Abstract

This paper describes an evaluation of automatic video summarization systems run on rushes from several BBC dramatic series. It was carried out under the auspices of the TREC Video Retrieval Evaluation (TRECVid) as a followup to the 2007 video summarization workshop held at ACM Multimedia 2007. 31 research teams submitted video summaries of 40 individual rushes video files, aiming to compress out redundant and insignificant material. Each summary had a duration of at most 2% of the original. The output of a baseline system, which simply presented each full video at 50 times normal speed was contributed by Carnegie Mellon University (CMU) as a control. The 2007 procedures for developing ground truth lists of important segments from each video were applied at the National Institute of Standards and Technology (NIST) to the BBC videos. At Dublin City University (DCU) each summary was judged by 3 humans with respect to how much of the ground truth was included and how well-formed the summary was. Additional objective measures included: how long it took the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the summary's duration was. Assessor agreement on finding desired segments averaged 81%. Results indicated that while it was still difficult to exceed the performance of the baseline on including ground truth, the baseline was outperformed by most other systems with respect to avoiding redundancy/junk and presenting the summary with a pleasant tempo/rhythm.

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Cited By

Saini PKumar KKashid SSaini ANegi A(2023)Video summarization using deep learning techniques: a detailed analysis and investigationArtificial Intelligence Review10.1007/s10462-023-10444-056:11(12347-12385)Online publication date: 15-Mar-2023
https://doi.org/10.1007/s10462-023-10444-0
Huang JWorring MGurrin CÞór Jónsson BKando NSchoeffmann KChen PO'Connor N(2020)Query-controllable Video SummarizationProceedings of the 2020 International Conference on Multimedia Retrieval10.1145/3372278.3390695(242-250)Online publication date: 8-Jun-2020
https://dl.acm.org/doi/10.1145/3372278.3390695
K. VSen DRaman B(2019)Video SkimmingACM Computing Surveys10.1145/334771252:5(1-38)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3347712
Show More Cited By

Index Terms

The trecvid 2008 BBC rushes summarization evaluation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

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

General Chairs:
Paul Over
NIST, USA
,
Alan F. Smeaton
Dublin City University, Ireland

Copyright © 2008 ACM.

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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Published: 31 October 2008

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October 31, 2008

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Cited By

Saini PKumar KKashid SSaini ANegi A(2023)Video summarization using deep learning techniques: a detailed analysis and investigationArtificial Intelligence Review10.1007/s10462-023-10444-056:11(12347-12385)Online publication date: 15-Mar-2023
https://doi.org/10.1007/s10462-023-10444-0
Huang JWorring MGurrin CÞór Jónsson BKando NSchoeffmann KChen PO'Connor N(2020)Query-controllable Video SummarizationProceedings of the 2020 International Conference on Multimedia Retrieval10.1145/3372278.3390695(242-250)Online publication date: 8-Jun-2020
https://dl.acm.org/doi/10.1145/3372278.3390695
K. VSen DRaman B(2019)Video SkimmingACM Computing Surveys10.1145/334771252:5(1-38)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3347712
Zhou HOtto CEwerth R(2019)Visual Summarization of Scholarly Videos Using Word Embeddings and Keyphrase ExtractionDigital Libraries for Open Knowledge10.1007/978-3-030-30760-8_28(327-335)Online publication date: 30-Aug-2019
https://doi.org/10.1007/978-3-030-30760-8_28
Kim HKim Y(2019)ERP/MMR Algorithm for Classifying Topic‐Relevant and Topic‐Irrelevant Visual Shots of Documentary VideosJournal of the Association for Information Science and Technology10.1002/asi.2417970:9(931-941)Online publication date: 2-Aug-2019
https://dl.acm.org/doi/10.1002/asi.24179
Rudinac SChua TDiaz-Ferreyra NFriedland GGornostaja THuet BKaptein RLindén KMoens MPeltonen JRedi MSchedl MShamma DSmeaton AXie L(2018)Rethinking Summarization and Storytelling for Modern Social MultimediaMultiMedia Modeling10.1007/978-3-319-73603-7_51(632-644)Online publication date: 13-Jan-2018
https://doi.org/10.1007/978-3-319-73603-7_51
Zlatintsi AKoutras PEvangelopoulos GMalandrakis NEfthymiou NPastra KPotamianos AMaragos P(2017)COGNIMUSE: a multimodal video database annotated with saliency, events, semantics and emotion with application to summarizationEURASIP Journal on Image and Video Processing10.1186/s13640-017-0194-12017:1Online publication date: 7-Aug-2017
https://doi.org/10.1186/s13640-017-0194-1
Putpuek NCooharojananone NSatoh S(2017)A modification of retake detection using simple signature and LCS algorithm2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)10.1109/SNPD.2017.8022730(257-261)Online publication date: Jun-2017
https://doi.org/10.1109/SNPD.2017.8022730
Ide IZhang YTanishige RDoman KKawanishi YDeguchi DMurase H(2017)Summarization of News Videos Considering the Consistency of Auditory and Visual Contents2017 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2017.33(193-199)Online publication date: Dec-2017
https://doi.org/10.1109/ISM.2017.33
Samani ZMoghaddam M(2017)A knowledge-based semantic approach for image collection summarizationMultimedia Tools and Applications10.1007/s11042-016-3840-176:9(11917-11939)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s11042-016-3840-1
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