Article

Practical elimination of near-duplicates from web video search

Authors:

Alexander G. Hauptmann,

Chong-Wah NgoAuthors Info & Claims

MM '07: Proceedings of the 15th ACM international conference on Multimedia

Pages 218 - 227

https://doi.org/10.1145/1291233.1291280

Published: 29 September 2007 Publication History

Abstract

Current web video search results rely exclusively on text keywords or user-supplied tags. A search on typical popular video often returns many duplicate and near-duplicate videos in the top results. This paper outlines ways to cluster and filter out the near-duplicate video using a hierarchical approach. Initial triage is performed using fast signatures derived from color histograms. Only when a video cannot be clearly classified as novel or near-duplicate using global signatures, we apply a more expensive local feature based near-duplicate detection which provides very accurate duplicate analysis through more costly computation. The results of 24 queries in a data set of 12,790 videos retrieved from Google, Yahoo! and YouTube show that this hierarchical approach can dramatically reduce redundant video displayed to the user in the top result set, at relatively small computational cost.

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Cited By

Fojcik KSyga PKlonowski M(2025)Extremely compact video representation for efficient near-duplicates detectionPattern Recognition10.1016/j.patcog.2024.111016158(111016)Online publication date: Feb-2025
https://doi.org/10.1016/j.patcog.2024.111016
Yan YRoychoudhury APaiva AAbreu RStorey M(2024)On Improving Management of Duplicate Video-Based Bug ReportsProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3639786(201-203)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3639786
Duan JHao YZhu BCheng LZhou PWang X(2024)Efficient Unsupervised Video Hashing With Contextual Modeling and Structural ControllingIEEE Transactions on Multimedia10.1109/TMM.2024.336892426(7438-7450)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3368924
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Index Terms

Practical elimination of near-duplicates from web video search
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Video summarization
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

MM '07: Proceedings of the 15th ACM international conference on Multimedia

September 2007

1115 pages

ISBN:9781595937025

DOI:10.1145/1291233

General Chairs:
Rainer Lienhart
University of Augsburg, Germany
,
Anand R. Prasad
DoCoMo Euro-Labs,Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, The Netherlands
,
Sunghyun Choi
Seoul National University, South Korea
,
Brian Bailey
University of Illinois at Urbana-Champaign
,
Nicu Sebe
University of Amsterdam, The Netherlands

Copyright © 2007 ACM.

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Publication History

Published: 29 September 2007

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MM07: The 15th ACM International Conference on Multimedia 2007

September 25 - 29, 2007

Augsburg, Germany

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Fojcik KSyga PKlonowski M(2025)Extremely compact video representation for efficient near-duplicates detectionPattern Recognition10.1016/j.patcog.2024.111016158(111016)Online publication date: Feb-2025
https://doi.org/10.1016/j.patcog.2024.111016
Yan YRoychoudhury APaiva AAbreu RStorey M(2024)On Improving Management of Duplicate Video-Based Bug ReportsProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3639786(201-203)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3639786
Duan JHao YZhu BCheng LZhou PWang X(2024)Efficient Unsupervised Video Hashing With Contextual Modeling and Structural ControllingIEEE Transactions on Multimedia10.1109/TMM.2024.336892426(7438-7450)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3368924
Dilawari AIqbal SSyed FMudassar Ilyas Q(2024)Deep Metric Learning for Near-Duplicate Video Retrieval Leveraging Efficient Semantic Feature ExtractionIEEE Access10.1109/ACCESS.2024.341110112(88897-88903)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3411101
Pizzi EKordopatis-Zilos GPatel HPostelnicu GNagavara Ravindra SGupta APapadopoulos STolias GDouze M(2024)The 2023 video similarity dataset and challengeComputer Vision and Image Understanding10.1016/j.cviu.2024.103997243(103997)Online publication date: Jun-2024
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Zachariah ARao P(2023)Video Retrieval for Everyday Scenes With Common ObjectsProceedings of the 2023 ACM International Conference on Multimedia Retrieval10.1145/3591106.3592239(565-570)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3591106.3592239
Liu WYang XLi ZQian FEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)SkipStreaming: Pinpointing User-Perceived Redundancy in Correlated Web Video Streaming through the Lens of ScenesProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611845(3944-3953)Online publication date: 26-Oct-2023
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Liu ZWeng YXu RChaomurilige Kim J(2023)An Efficient Machine-Generated Data Modeling Approach Based on Domain-Aware Knowledge for Intelligent Consumer ElectronicsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332721669:4(984-995)Online publication date: Nov-2023
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Li XWang ZLiu YFan QChen J(2023)A Secure Client Video Deduplication Scheme Based on 3D CNN2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)10.1109/MLCCIM60412.2023.00030(165-176)Online publication date: 25-Jul-2023
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Deng RWu QLi Y(2023)3D-CSL: Self-Supervised 3D Context Similarity Learning for Near-Duplicate Video Retrieval2023 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP49359.2023.10222915(2880-2884)Online publication date: 8-Oct-2023
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