short-paper

Which Information Sources are More Effective and Reliable in Video Search

Authors:

Alexander G. HauptmannAuthors Info & Claims

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 1069 - 1072

https://doi.org/10.1145/2911451.2914765

Published: 07 July 2016 Publication History

Abstract

It is common that users are interested in finding video segments, which contain further information about the video contents in a segment of interest. To facilitate users to find and browse related video contents, video hyperlinking aims at constructing links among video segments with relevant information in a large video collection. In this study, we explore the effectiveness of various video features on the performance of video hyperlinking, including subtitle, metadata, content features (i.e., audio and visual), surrounding context, as well as the combinations of those features. Besides, we also test different search strategies over different types of queries, which are categorized according to their video contents. Comprehensive experimental studies have been conducted on the dataset of TRECVID 2015 video hyperlinking task. Results show that (1) text features play a crucial role in search performance, and the combination of audio and visual features cannot provide improvements; (2) the consideration of contexts cannot obtain better results; and (3) due to the lack of training examples, machine learning techniques cannot improve the performance.

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Cited By

Pan WZhao ZHuang WZhang ZFu LPan ZYu JWu F(2024)Video Moment Retrieval With Noisy LabelsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321290035:5(6779-6791)Online publication date: May-2024
https://doi.org/10.1109/TNNLS.2022.3212900
Xiong GMeng MZhang TZhang DZhang Y(2024)Reference-Aware Adaptive Network for Image-Text MatchingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.339261934:10(9678-9691)Online publication date: Oct-2024
https://doi.org/10.1109/TCSVT.2024.3392619
Li ZZhang LZhang KZhang YMao Z(2024)Improving Image-Text Matching With Bidirectional Consistency of Cross-Modal AlignmentIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.336965634:7(6590-6607)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2024.3369656
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Index Terms

Which Information Sources are More Effective and Reliable in Video Search
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Retrieval effectiveness
    2. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Video search

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Published In

cover image ACM Conferences

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

Copyright © 2016 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2016

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SIGIR

SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

Acceptance Rates

SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Pan WZhao ZHuang WZhang ZFu LPan ZYu JWu F(2024)Video Moment Retrieval With Noisy LabelsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321290035:5(6779-6791)Online publication date: May-2024
https://doi.org/10.1109/TNNLS.2022.3212900
Xiong GMeng MZhang TZhang DZhang Y(2024)Reference-Aware Adaptive Network for Image-Text MatchingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.339261934:10(9678-9691)Online publication date: Oct-2024
https://doi.org/10.1109/TCSVT.2024.3392619
Li ZZhang LZhang KZhang YMao Z(2024)Improving Image-Text Matching With Bidirectional Consistency of Cross-Modal AlignmentIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.336965634:7(6590-6607)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2024.3369656
Zhu YLi XZheng MYang JWang ZGuo XChai ZYuan YJiang S(2023)Focus and Align: Learning Tube Tokens for Video-Language Pre-TrainingIEEE Transactions on Multimedia10.1109/TMM.2022.323110825(8036-8050)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3231108
Zhang HYang YQi FQian SXu C(2023)Debiased Video-Text Retrieval via Soft Positive Sample CalibrationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.324887333:9(5257-5270)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3248873
Patel AMerlino GBruneo DPuliafito AVyas OOjha M(2021)Video representation and suspicious event detection using semantic technologiesSemantic Web10.3233/SW-20039312:3(467-491)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/SW-200393
Nie LJia MSong XWu GCheng HGu JDiaz FShah CSuel TCastells PJones RSakai T(2021)Multimodal Activation: Awakening Dialog Robots without Wake WordsProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462964(491-500)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462964
Zhu WZang JTobita H(2020)Wordy: Interactive Word Cloud to Summarize and Browse Online Videos to Enhance eLearning2020 IEEE/SICE International Symposium on System Integration (SII)10.1109/SII46433.2020.9026306(879-884)Online publication date: Jan-2020
https://doi.org/10.1109/SII46433.2020.9026306
Cao DYu ZZhang HFang JNie LTian QAmsaleg LHuet BLarson MGravier GHung HNgo CTsang Ooi W(2019)Video-Based Cross-Modal Recipe RetrievalProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3351067(1685-1693)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3343031.3351067
Jiang BHuang XYang CYuan JEl Saddik ADel Bimbo AZhang ZHauptmann ACandan KBertini MXie LWei X(2019)Cross-Modal Video Moment Retrieval with Spatial and Language-Temporal AttentionProceedings of the 2019 on International Conference on Multimedia Retrieval10.1145/3323873.3325019(217-225)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3323873.3325019
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