Article

Multimedia search with pseudo-relevance feedback

Authors:

Alexander Hauptmann,

Rong JinAuthors Info & Claims

CIVR'03: Proceedings of the 2nd international conference on Image and video retrieval

Pages 238 - 247

Published: 24 July 2003 Publication History

Abstract

We present an algorithm for video retrieval that fuses the decisions of multiple retrieval agents in both text and image modalities. While the normalization and combination of evidence is novel, this paper emphasizes the successful use of negative pseudorelevance feedback to improve image retrieval performance. Although we have not solved all problems in video information retrieval, the results are encouraging, indicating that pseudo-relevance feedback shows great promise for multimedia retrieval with very varied and errorful data.

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Cited By

Li JXu CYang WSun CTao D(2017)Discriminative Multi-View Interactive Image Re-RankingIEEE Transactions on Image Processing10.1109/TIP.2017.265137926:7(3113-3127)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2651379
Boteanu BMironicăź IIonescu B(2017)Pseudo-relevance feedback diversification of social image retrieval resultsMultimedia Tools and Applications10.1007/s11042-016-3678-676:9(11889-11916)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s11042-016-3678-6
Lin BWei ATian X(2016)Visual Re-ranking Through Greedy Selection and Rank FusionProceedings, Part I, of the 22nd International Conference on MultiMedia Modeling - Volume 951610.1007/978-3-319-27671-7_24(289-300)Online publication date: 4-Jan-2016
https://dl.acm.org/doi/10.1007/978-3-319-27671-7_24
Show More Cited By

Multimedia search with pseudo-relevance feedback
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

cover image Guide Proceedings

CIVR'03: Proceedings of the 2nd international conference on Image and video retrieval

July 2003

512 pages

ISBN:3540406344

Editors:
Erwin M. Bakker
Leiden University, LIACS Media Lab, Leiden, The Netherlands
,
Michael S. Lew
Leiden University, LIACS Media Lab, Leiden, The Netherlands
,
Thomas S. Huang
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL
,
Nicu Sebe
University of Amsterdam, Amsterdam, The Netherlands
,
Xiang Zhou
Siemens Corporate Research, Princeton, NJ

Sponsors

Leiden University: Leiden University
Tsinghua University: TsingHua University Philips Research
University of Illinois: University of Illinois
The Beckman Institute

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 24 July 2003

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Cited By

Li JXu CYang WSun CTao D(2017)Discriminative Multi-View Interactive Image Re-RankingIEEE Transactions on Image Processing10.1109/TIP.2017.265137926:7(3113-3127)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2651379
Boteanu BMironicăź IIonescu B(2017)Pseudo-relevance feedback diversification of social image retrieval resultsMultimedia Tools and Applications10.1007/s11042-016-3678-676:9(11889-11916)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s11042-016-3678-6
Lin BWei ATian X(2016)Visual Re-ranking Through Greedy Selection and Rank FusionProceedings, Part I, of the 22nd International Conference on MultiMedia Modeling - Volume 951610.1007/978-3-319-27671-7_24(289-300)Online publication date: 4-Jan-2016
https://dl.acm.org/doi/10.1007/978-3-319-27671-7_24
Lin WChen ZKe STsai CLin W(2015)The effect of low-level image features on pseudo relevance feedbackNeurocomputing10.1016/j.neucom.2015.04.037166:C(26-37)Online publication date: 20-Oct-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.04.037
Tsai CHu YChen Z(2015)Factors affecting rocchio-based pseudorelevance feedback in image retrievalJournal of the Association for Information Science and Technology10.1002/asi.2315466:1(40-57)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1002/asi.23154
Jiang LMeng DMitamura THauptmann AHua KRui YSteinmetz RHanjalic ANatsev AZhu W(2014)Easy Samples FirstProceedings of the 22nd ACM international conference on Multimedia10.1145/2647868.2654918(547-556)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2647868.2654918
Cui PLiu SZhu WLuan HChua TYang S(2014)Social-Sensed Image SearchACM Transactions on Information Systems10.1145/259097432:2(1-23)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1145/2590974
Jiang LMitamura TYu SHauptmann AKankanhalli MRueger SManmatha RJose Jvan Rijsbergen K(2014)Zero-Example Event Search using MultiModal Pseudo Relevance FeedbackProceedings of International Conference on Multimedia Retrieval10.1145/2578726.2578764(297-304)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1145/2578726.2578764
Nie LZhao YWang XShen JChua T(2014)Learning to Recommend Descriptive Tags for Questions in Social ForumsACM Transactions on Information Systems10.1145/255915732:1(1-23)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1145/2559157
Mei TRui YLi STian Q(2014)Multimedia search rerankingACM Computing Surveys10.1145/253679846:3(1-38)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1145/2536798
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