Article

A novel framework for SVM-based image retrieval on large databases

Authors:

Lei Wang,

Xuchun Li,

Ping Xue,

Kap Luk ChanAuthors Info & Claims

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

Pages 487 - 490

https://doi.org/10.1145/1101149.1101258

Published: 06 November 2005 Publication History

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Abstract

In this paper, a novel framework is proposed to deliver a fast, robust, and generally applicable SVM-based image retrieval for large databases. A quick test scheme is developed, and on-line kernel learning is employed to realize it after analyzing the relationship between them. Then an upper bound on maximum test scope is derived to speed up testing further. Also, the general applicability is well maintained because this framework does not need a kernel function and index structure to be pre-defined. Taking the advantages of this framework, more sophisticated SVM can be used to improve retrieval performance while keeping short response time. Experimental results on large image databases verify the effectiveness and efficiency of the proposed framework.

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Cited By

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Thomee BLew M(2012)Interactive search in image retrieval: a surveyInternational Journal of Multimedia Information Retrieval10.1007/s13735-012-0014-41:2(71-86)Online publication date: 8-Jun-2012
https://doi.org/10.1007/s13735-012-0014-4
Liu LWang LCandan KPanchanathan SPrabhakaran BSundaram HFeng WSebe N(2011)Exploring latent class information for image retrieval using the bag-of-feature modelProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072026(1405-1408)Online publication date: 28-Nov-2011
https://dl.acm.org/doi/10.1145/2072298.2072026
Li JAllinson N(2008)Long‐term learning in content‐based image retrievalInternational Journal of Imaging Systems and Technology10.1002/ima.2014818:2-3(160-169)Online publication date: 11-Aug-2008
https://doi.org/10.1002/ima.20148

Index Terms

A novel framework for SVM-based image retrieval on large databases
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Retrieval models and ranking

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Information

Published In

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

November 2005

1110 pages

ISBN:1595930442

DOI:10.1145/1101149

General Chairs:
Hongjiang Zhang
Microsoft Research Asia, China
,
Tat-Seng Chua
National University of Singapore, Singapore
,
Program Chairs:
Ralf Steinmetz
Technische Universitat Darmstadt, Germany
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Lynn Wilcox
FXPAL

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2005

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MM05: 2005 13th Annual ACM International Conference on Multimedia

November 6 - 11, 2005

Hilton, Singapore

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MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

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Thomee BLew M(2012)Interactive search in image retrieval: a surveyInternational Journal of Multimedia Information Retrieval10.1007/s13735-012-0014-41:2(71-86)Online publication date: 8-Jun-2012
https://doi.org/10.1007/s13735-012-0014-4
Liu LWang LCandan KPanchanathan SPrabhakaran BSundaram HFeng WSebe N(2011)Exploring latent class information for image retrieval using the bag-of-feature modelProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072026(1405-1408)Online publication date: 28-Nov-2011
https://dl.acm.org/doi/10.1145/2072298.2072026
Li JAllinson N(2008)Long‐term learning in content‐based image retrievalInternational Journal of Imaging Systems and Technology10.1002/ima.2014818:2-3(160-169)Online publication date: 11-Aug-2008
https://doi.org/10.1002/ima.20148

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