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.

References

[1]

D. A. Adjeroh, M. C. Lee, and I. King. A Distance Measure for Video Sequences. CVIU, pp. 25--45, 1999.

Digital Library

[2]

J. Allan, editor. Topic Detection and Tracking: Event-based Information Organization. Kluwer Academic Publishers, 2002.

Digital Library

[3]

J. Allan, C. Wade, and A. Bolivar. Retrieval and Novelty Detection at the Sentence Level. ACM SIGIR'03.

Digital Library

[4]

T. Brants, F. Chen, and A. Farahat. A System for New Event Detection. ACM SIGIR'03, Canada, Jul. 2003.

Digital Library

[5]

J. Carbonell and J. Goldstein. The Use of MMR, Diversity-based Reranking for Reordering Documents and Producing Summaries. ACM SIGIR'98.

Digital Library

[6]

S.-F. Chang, W. Hsu, L. Kennedy, L. Xie and et al. Columbia University TRECVID-2005 Video Search and High-Level Feature Extraction. TRECVID 2005, Washington DC, 2005.

[7]

S. C. Cheung and A. Zakhor. Efficient Video Similarity Measurement with Video Signature. IEEE Trans. on CSVT, vol. 13, no. 1, pp. 59--74, Jan. 2003.

Digital Library

[8]

S. C. Cheung and A. Zakhor. Fast Similarity Search and Clustering of Video Sequences on the World-Wide-Web. IEEE Trans. on CSVT, vol. 7, no. 3, pp. 524--537, June 2005.

Digital Library

[9]

E. Gabrilovich, S. Dumais, and E. Horvitz. Newsjunkie: Providing Personalized Newsfeeds via Analysis of Information Novelty. WWW'04, USA, 2004, pp. 482--490.

Digital Library

[10]

Google Video. Available: http://video.google.com.

[11]

A. Hampapur and R. Bolle. Comparison of Sequence Matching Techniques for Video Copy Detection. Conf. on Storage and Retrieval for Media Databases, 2002.

[12]

T. C. Hoad and J. Zobel. Fast Video Matching with Signature Alignment. MIR'03, pp. 262--269, USA, 2003.

Digital Library

[13]

W. H. Hsu, L. S. Kennedy and S.-F. Chang. Video Search Reranking via Information Bottleneck Principle. ACM MM'06, USA, pp. 35--44, 2006.

Digital Library

[14]

Informedia. Available: http://www.informedia.cs.cmu.edu.

[15]

A. Jaimes. Conceptual Structures and Computational Methods for Indexing and Organization of Visual Information. Ph.D. Thesis, 2003.

Digital Library

[16]

A. K. Jain, A. Vailaya, and W. Xiong. Query by Video Clip. ACM Multimedia Syst. J., vol. 7, pp. 369--384, 1999.

Digital Library

[17]

A. Joly, O. Buisson and C. Frelicot. Content-based Copy Retrieval Using Distortion-based Probabilistic Similarity Search. IEEE Trans. on MM, vol. 9, no. 2, Feb. 2007.

Digital Library

[18]

K. Kashino, Takayuki, and H. Murase. A Quick Search Method for Audio and Video Signals Based on Histogram Pruning. IEEE Trans. on MM, vol. 5, no. 3, 2003.

Digital Library

[19]

Y. Ke, R. Sukthankar, and L. Huston. Efficient Near-Duplicate Detection and Sub-Image Retrieval. ACM MM'04.

Digital Library

[20]

J. Law-To, B. Olivier, V. Gouet-Brunet and B. Nozha. Robust Voting Algorithm Based on Labels of Behavior for Video Copy Detection. ACM MM'06, pp. 835--844, 2006.

Digital Library

[21]

R. Lienhart and W. Effelsberg. VisualGREP: A Systematic Method to Compare and Retrieve Video Sequences. Multimedia Tools Appl., vol. 10, no. 1, pp. 47--72, Jan. 2000.

Digital Library

[22]

L. Liu, W. Lai, X.-S. Hua, and S.-Q. Yang. Video Histogram: A Novel Video Signature for Efficient Web Video Duplicate Detection. MMM'07.

Digital Library

[23]

X. Liu, Y. Zhuang, and Y. Pan. A New Approach to Retrieve Video by Example Video Clip. ACM MM'99, 1999.

Digital Library

[24]

D. Lowe. Distinctive Image Features from Scale-Invariant Key Points. IJCV, vol. 60, pp. 91--110, 2004.

Digital Library

[25]

K. Mikolajczyk and C. Schmid. A Performance Evaluation of Local Descriptors. CVPR'03, pp. 257--263.

Digital Library

[26]

K. Mikolajczyk and C. Schmid. Scale and Affine Invariant Interest Point Detectors. IJCV, 60 (2004), pp. 63--86.

Digital Library

[27]

C.-W. Ngo, W.-L. Zhao, Y.-G. Jiang. Fast Tracking of Near-Duplicate Keyframes in Broadcast Domain with Transitivity Propagation. ACM MM'06, pp. 845--854, USA, Oct. 2006.

Digital Library

[28]

Y. Peng and C.-W. Ngo. Clip-based Similarity Measure for Query-Dependent Clip Retrieval and Video Summarization. IEEE Trans. on CSVT, vol. 16, no. 5, May 2006.

Digital Library

[29]

Wikipedia. http://en.wikipedia.org/wiki/Youtube.

[30]

X. Wu, A. G. Hauptmann, and C.-W. Ngo. Novelty Detection for Cross-Lingual News Stories with Visual Duplicates and Speech Transcripts. ACM MM'07.

Digital Library

[31]

X. Wu, C.-W. Ngo, and Q. Li. Threading and Autodocumenting News Videos. IEEE Signal Processing Magazine, vol. 23, no. 2, pp. 59--68, March 2006.

[32]

Yahoo! Video. Available: http://video.yahoo.com.

[33]

Y. Yang, J. Zhang, J. Carbonell and C. Jin. Topic-conditioned Novelty Detection. ACM SIGKDD'02, Canada.

Digital Library

[34]

YouTube. Available: http://www.youtube.com.

[35]

J. Yuan, L.-Y. Duan, Q. Tian, S. Ranganath and C. Xu. Fast and Robust Short Video Clip Search for Copy Detection. Pacific Rim Conf. on Multimedia (PCM), 2004.

Digital Library

[36]

C. Zhai, W. Cohen and J. Lafferty. Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. ACM SIGIR'03.

Digital Library

[37]

B. Zhang et. al. Improving Web Search Results Using Affinity Graph. ACM SIGIR.05.

Digital Library

[38]

D.-Q. Zhang and S.-F. Chang. Detecting Image Near-Duplicate by Stochastic Attributed Relational Graph Matching with Learning. ACM MM'04, USA, Oct. 2004.

Digital Library

[39]

Y. Zhang, J. Callan, and T. Minka. Novelty and Redundancy Detection in Adaptive Filtering. ACM SIGIR'02, 2002.

Digital Library

Cited By

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
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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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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 995 of 4,171 submissions, 24%

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Cited By

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
https://doi.org/10.1016/j.cviu.2024.103997
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
https://dl.acm.org/doi/10.1145/3581783.3611845
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
https://doi.org/10.1109/TCE.2023.3327216
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
https://doi.org/10.1109/MLCCIM60412.2023.00030
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
https://doi.org/10.1109/ICIP49359.2023.10222915
Black AJenni SBui TTanjim MPetrangeli SSinha RSwaminathan VCollomosse J(2023)VADER: Video Alignment Differencing and Retrieval2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.02043(22300-22310)Online publication date: 1-Oct-2023
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