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Concept representation based video indexing

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SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Pages 654 - 655

https://doi.org/10.1145/1571941.1572062

Published: 19 July 2009 Publication History

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Abstract

This poster introduces a novel concept-based video indexing approach. It is developed based on a rich set of base concepts, of which the models are available. Then, for a given concept with several labeled samples, we combine the base concepts to fit it and its model can thus be obtained accordingly. Empirical results demonstrate that this method can achieve great performance even with very limited labeled data. We have compared different representation approaches including both sparse and non-sparse methods. Our conclusion is that the sparse method will lead to much better performance.

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TRECVID: TREC Video Retrieval Evaluation, http://www-nlpir.nist.gov/projects/trecvid.

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A. Hauptmann, M. Y. Chen, and M. Christel. Confounded expectations: Informedia at trecvid 2004. In Proceedings of TREC Video Retrieval Evaluation, 2004.

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M. R. Naphade, L. Kennedy, J. R. Kender, S.-F. Chang, J. R. Smith, P. Over, and A. Hauptmann. A light scale concept ontology for multimedia understanding for TRECVID 2005.

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J. R. Smith and M. Naphade. Multimedia semantic indexing using model vectors. In Proceedings of International Conference on Multimedia and Expo, 2003.

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A. Yanagawa, S.-F. Chang, L. Kennedy, and W. Hsu. Columbia university's baseline detectors for 374 LSCOM semantic visual concepts. Columbia University ADVENT Technical Report #222-2006-8, 2007.

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Cited By

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Geng BLi YTao DWang MZha ZXu C(2012)Parallel Lasso for Large-Scale Video Concept DetectionIEEE Transactions on Multimedia10.1109/TMM.2011.217478114:1(55-65)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1109/TMM.2011.2174781
Mu YChen XLiu XChua TYan S(2012)Multimedia semantics-aware query-adaptive hashing with bits reconfigurabilityInternational Journal of Multimedia Information Retrieval10.1007/s13735-012-0003-71:1(59-70)Online publication date: 9-Mar-2012
https://doi.org/10.1007/s13735-012-0003-7
Mu YChen XChua TYan SDe Natale FDel Bimbo AHanjalic AManjunath BSatoh S(2011)Learning reconfigurable hashing for diverse semanticsProceedings of the 1st ACM International Conference on Multimedia Retrieval10.1145/1991996.1992003(1-8)Online publication date: 18-Apr-2011
https://dl.acm.org/doi/10.1145/1991996.1992003

Index Terms

Concept representation based video indexing
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

July 2009

896 pages

ISBN:9781605584836

DOI:10.1145/1571941

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Javed Aslam
Northeastern University, USA
,
Program Chairs:
Mark Sanderson
University of Sheffield, UK
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Justin Zobel
University of Melbourne, Australia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2009

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SIGIR '09: The 32nd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2009

MA, Boston, USA

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Geng BLi YTao DWang MZha ZXu C(2012)Parallel Lasso for Large-Scale Video Concept DetectionIEEE Transactions on Multimedia10.1109/TMM.2011.217478114:1(55-65)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1109/TMM.2011.2174781
Mu YChen XLiu XChua TYan S(2012)Multimedia semantics-aware query-adaptive hashing with bits reconfigurabilityInternational Journal of Multimedia Information Retrieval10.1007/s13735-012-0003-71:1(59-70)Online publication date: 9-Mar-2012
https://doi.org/10.1007/s13735-012-0003-7
Mu YChen XChua TYan SDe Natale FDel Bimbo AHanjalic AManjunath BSatoh S(2011)Learning reconfigurable hashing for diverse semanticsProceedings of the 1st ACM International Conference on Multimedia Retrieval10.1145/1991996.1992003(1-8)Online publication date: 18-Apr-2011
https://dl.acm.org/doi/10.1145/1991996.1992003

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Sparse representation-based robust face recognition by graph regularized low-rank sparse representation recovery

An antinoise sparse representation method for robust face recognition via joint l1 and l2 regularization

A Comparative Study on the Use of Multi-label Classification Techniques for Concept-Based Video Indexing and Annotation

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