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

Get Access

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.

References

[1]

http://vision.ece.ucsb.edu/datasets/.]]

Google Scholar

[2]

A. Dong and B. Bhanu. A New Semi-supervised EM Algorithm for Image Retrieval. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2003.]]

Google Scholar

[3]

G. Guo, A. K. Jain, W.-Y. Ma, and H. Zhang. Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback. IEEE Transactions on Neural Networks, 13(4):811--820, 2002.]]

Digital Library

Google Scholar

[4]

C.-H. Hoi and M. R. Lyu. A novel log-based relevance feedback technique in content-based image retrieval. In Proceedings of the 12th annual ACM international conference on Multimedia, pages 24--31, 2004.]]

Digital Library

Google Scholar

[5]

J. Kivinen, A. J. Smola, and R. C. Williamson. Online learning with kernels. IEEE Transactions on Signal Processing, 52(8):2165--2176, Aug 2004.]]

Digital Library

Google Scholar

[6]

N. Panda, K. Goh, and E. Y. Chang. Active Learning in Very Large Image Databases. Journal of Multimedia Tools and Applications Special Issue on Computer Vision Meets Databases (Accepted).]]

Google Scholar

[7]

J. Peng and D. R. Heisterkamp. Kernel Indexing for Relevance Feedback Image Retrieval. Proceedings of International Conference on Image Processing, 2003.]]

Digital Library

Google Scholar

[8]

S. Tong and E. Chang. Support Vector Machine Active Learning for Image Retrieval. Proceedings of ACM International Conference on Multimedia, pages 107--118, Ottawa, Canada, Oct, 2001.]]

Digital Library

Google Scholar

[9]

L. Wang, P. Xue, and K. L. Chan. Incorporating Prior Knowledge into SVM for Image Retrieval. Proceedings of International Conference on Pattern Recognition (ICPR), UK, 2004.]]

Digital Library

Google Scholar

Cited By

View all

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

Recommendations

A novel log-based relevance feedback technique in content-based image retrieval
MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

Relevance feedback has been proposed as an important technique to boost the retrieval performance in content-based image retrieval (CBIR). However, since there exists a semantic gap between low-level features and high-level semantic concepts in CBIR, ...
Ensemble one-class support vector machines for content-based image retrieval

In order to narrow semantic gap between user query concept and low-level features in content-based image retrieval, SVM-based relevance feedback techniques are developed to learn user's query concept by labeling some samples. The major difficulty in ...
Relevance Feedback for Content-Based Image Retrieval Using Support Vector Machines and Feature Selection
ICANN '09: Proceedings of the 19th International Conference on Artificial Neural Networks: Part I

A relevance feedback (RF) approach for content-based image retrieval (CBIR) is proposed, which is based on Support Vector Machines (SVMs) and uses a feature selection technique to reduce the dimensionality of the image feature space. Specifically, each ...

Comments

Information & Contributors

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

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2005

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

MM05

Sponsor:

MM05: 2005 13th Annual ACM International Conference on Multimedia

November 6 - 11, 2005

Hilton, Singapore

Acceptance Rates

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

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

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
274
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

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

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

A novel log-based relevance feedback technique in content-based image retrieval

Ensemble one-class support vector machines for content-based image retrieval

Relevance Feedback for Content-Based Image Retrieval Using Support Vector Machines and Feature Selection

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations