Article

The combination limit in multimedia retrieval

Authors:

Rong Yan,

Alexander G. HauptmannAuthors Info & Claims

MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on Multimedia

Pages 339 - 342

https://doi.org/10.1145/957013.957086

Published: 02 November 2003 Publication History

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Abstract

Combining search results from multimedia sources is crucial for dealing with heterogeneous multimedia data, particularly in multimedia retrieval where a final ranked list of items of interest is returned sorted by confidence or relevance. However, relatively little attention has been given to combination functions, especially their upper bound performance limits. This paper presents a theoretical framework for studying upper bounds for two types of combination functions. A general upper bound and two approximations are proposed for monotonic combination functions. We also studied the upper bounds for linear combination functions using a global optimization technique. Our experimental results show that the choice of combination functions has a considerable influence to retrieval performance.

References

[1]

C. Dwork, S. R. Kumar, M. Naor, and D. Sivakumar. Rank aggregation methods for the web. In World Wide Web, pages 613--622, 2001.

Digital Library

Google Scholar

[2]

R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. In ACM Symposium on Principles of Database Systems, 2001.

Digital Library

Google Scholar

[3]

D. Hush and B. Horne. Efficient algorithms for function approximation with piecewise linear sigmoidal networks. IEEE Trans. Neural Networks, 9(6), 1998.

Digital Library

Google Scholar

[4]

W. Huyer and A. Neumaier. Global optimization by multilevel coordinate search. Journal Global Optimization, 14, 1999.

Digital Library

Google Scholar

[5]

M. Naphade and et~al. Probabilistic multimedia objects (multijects): A novel approach to video indexing and retrieval in multimedia systems. In Proc. of ICIP, 1998.

Crossref

Google Scholar

[6]

J. R. Smith and et~al. Interactive search fusion methods for video database retrieval. In IEEE Intl. Conf. on Image Processing, Barcelona, Spain, 2003.

Crossref

Google Scholar

[7]

TREC2002. TREC2002 video track, http://www-nlpir.nist.gov/projects/t2002v/t2002v.html.

Google Scholar

[8]

R. Yan, A. Hauptmann, and R. Jin. Multimedia search with pseudo-relevance feedback. In International Conference on Image and Video Retrieval, Urbana, IL, USA, 2003.

Digital Library

Google Scholar

Cited By

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Gavrilova MLuchak ISudhakar TTumpa S(2022)Artificial Intelligence in Biometrics: Uncovering Intricacies of Human Body and MindAdvances in Selected Artificial Intelligence Areas10.1007/978-3-030-93052-3_7(123-169)Online publication date: 27-Feb-2022
https://doi.org/10.1007/978-3-030-93052-3_7
Yazici AKoyuncu MSert SYilmaz T(2019)A Fusion-Based Framework for Wireless Multimedia Sensor Networks in Surveillance ApplicationsIEEE Access10.1109/ACCESS.2019.29262067(88418-88434)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2926206
Mazaheri AGong BShah M(2018)Learning a Multi-Concept Video Retrieval Model with Multiple Latent VariablesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/317664714:2(1-21)Online publication date: 25-Apr-2018
https://dl.acm.org/doi/10.1145/3176647
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Index Terms

The combination limit in multimedia retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information

Published In

MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on Multimedia

November 2003

670 pages

ISBN:1581137222

DOI:10.1145/957013

General Chairs:
Lawrence Rowe
University of California, Berkeley
,
Harrick Vin
University of Texas, Austin
,
Program Chairs:
Thomas Plagemann
University of Oslo
,
Prashant Shenoy
University of Massachusetts, Amherst
,
John R. Smith
IBM T.J. Watson Research Center

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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Association for Computing Machinery

New York, NY, United States

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Published: 02 November 2003

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MM03: 2003 11th Annual ACM International Conference on Multimedia

November 2 - 8, 2003

CA, Berkeley, USA

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

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Gavrilova MLuchak ISudhakar TTumpa S(2022)Artificial Intelligence in Biometrics: Uncovering Intricacies of Human Body and MindAdvances in Selected Artificial Intelligence Areas10.1007/978-3-030-93052-3_7(123-169)Online publication date: 27-Feb-2022
https://doi.org/10.1007/978-3-030-93052-3_7
Yazici AKoyuncu MSert SYilmaz T(2019)A Fusion-Based Framework for Wireless Multimedia Sensor Networks in Surveillance ApplicationsIEEE Access10.1109/ACCESS.2019.29262067(88418-88434)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2926206
Mazaheri AGong BShah M(2018)Learning a Multi-Concept Video Retrieval Model with Multiple Latent VariablesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/317664714:2(1-21)Online publication date: 25-Apr-2018
https://dl.acm.org/doi/10.1145/3176647
Hamadi ALattar HKhoussa MSafadi B(2018)Using semantic context for multiple concepts detection in still imagesPattern Analysis and Applications10.1007/s10044-018-0761-9Online publication date: 2-Nov-2018
https://doi.org/10.1007/s10044-018-0761-9
Li ZLi Z(2017)Understanding-Oriented Multimedia News RetrievalUnderstanding-Oriented Multimedia Content Analysis10.1007/978-981-10-3689-7_5(101-129)Online publication date: 27-May-2017
https://doi.org/10.1007/978-981-10-3689-7_5
Shen JShen JMei TGao X(2016)Landmark Reranking for Smart Travel Guide Systems by Combining and Analyzing Diverse MediaIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2016.252394846:11(1492-1504)Online publication date: Nov-2016
https://doi.org/10.1109/TSMC.2016.2523948
Mazaheri AGong BShah M(2016)Learning a Multi-concept Video Retrieval Model with Multiple Latent Variables2016 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2016.0132(615-620)Online publication date: Dec-2016
https://doi.org/10.1109/ISM.2016.0132
Hamadi AMulhem PQuénot G(2016)A comparative study for multiple visual concepts detection in images and videosMultimedia Tools and Applications10.1007/s11042-015-2730-275:15(8973-8997)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s11042-015-2730-2
Yang XZhang YYao TNgo CMei T(2015)Click-boosting multi-modality graph-based reranking for image searchMultimedia Systems10.1007/s00530-014-0379-821:2(217-227)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1007/s00530-014-0379-8
Hamadi AMulhem PQuénot GKankanhalli MRueger SManmatha RJose Jvan Rijsbergen K(2014)Infrequent concept pairs detection in multimedia documentsProceedings of International Conference on Multimedia Retrieval10.1145/2578726.2578787(435-438)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1145/2578726.2578787
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XML Multimedia Retrieval: From Relevant Textual Information to Relevant Multimedia Fragments

Adaptive combination of evidence for information retrieval

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