research-article

Learning Similarity Matching in Multimedia Content-Based Retrieval

Authors:

Joo-Hwee Lim,

Jian Kang Wu,

Sumeet Singh,

Desai NarasimhaluAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 13, Issue 5

Pages 846 - 850

https://doi.org/10.1109/69.956107

Published: 01 September 2001 Publication History

Publisher Site

Abstract

Many multimedia content-based retrieval systems allow query formulation with user setting of relative importance of features (e.g., color, texture, shape, etc) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. In this paper, we present a neural network-based learning algorithm for adapting similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results.

References

[1]

F. Crestani and C.J. van Rijsbergen, ”A Model for Adaptive Information Retrieval,” J. Intelligent Information Systems, vol. 8, pp. 29-56, 1997.

Digital Library

Google Scholar

[2]

R.O. Duda and P.E. Hart, Pattern Classification and Scence Analysis. John Wiley & Sons, 1973.

Google Scholar

[3]

M. Flickner, et al., ”Query by Image and Video Content: The QBIC System,” Computer, vol. 28, no. 9, pp. 23-32, Sept. 1995.

Digital Library

Google Scholar

[4]

J. Hertz A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation. Addison-Wesley, 1991.

Digital Library

Google Scholar

[5]

R. Jain, “Infoscopes: Multimedia Iinformation Systems,” Multimedia Systems and Techniques. B. Furht, ed., pp. 217-253, Kluwer Academic Publishers, 1996

Google Scholar

[6]

S. Santini and R. Jain, “Similarity Matching,” Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 871-883, Sept. 1999.

Digital Library

Google Scholar

[7]

Virage, http://www.virage.com.

Google Scholar

[8]

J.K. Wu, et al. “Content-Based Retrieval for Trademark Registration,” Multimedia Tools and Applications, vol. 3, no. 3, pp. 245-267, 1996.

Crossref

Google Scholar

Cited By

View all

Eric Hostler RYoon VGuimaraes T(2012)Recommendation agent impact on consumer online shoppingExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.08.16039:3(2989-2999)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.08.160
Yin PBhanu BChang KDong A(2005)Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2005.20127:10(1536-1551)Online publication date: 1-Oct-2005
https://dl.acm.org/doi/10.1109/TPAMI.2005.201
Bhandarkar SChen F(2005)Similarity Analysis of Video Sequences Using an Artificial Neural NetworkApplied Intelligence10.1007/s10791-005-6622-322:3(251-275)Online publication date: 1-May-2005
https://dl.acm.org/doi/10.1007/s10791-005-6622-3

Index Terms

Learning Similarity Matching in Multimedia Content-Based Retrieval
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

Recommendations

Content-based multimedia retrieval in the presence of unknown user preferences
MMM'11: Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I

Content-based multimedia retrieval requires an appropriate similarity model which reflects user preferences. When these preferences are unknown or when the structure of the data collection is unclear, retrieving the most preferable objects the user has ...
A Survey on Content-Based Retrieval for Multimedia Databases

Conventional database systems are designed for managing textual and numerical data, and retrieving such data is often based on simple comparisons of text/numerical values. However, this simple method of retrieval is no longer adequate for the multimedia ...
Relevance feature mapping for content-based multimedia information retrieval

This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 13, Issue 5

September 2001

144 pages

ISSN:1041-4347

Issue’s Table of Contents

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 September 2001

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 30 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Eric Hostler RYoon VGuimaraes T(2012)Recommendation agent impact on consumer online shoppingExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.08.16039:3(2989-2999)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.08.160
Yin PBhanu BChang KDong A(2005)Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2005.20127:10(1536-1551)Online publication date: 1-Oct-2005
https://dl.acm.org/doi/10.1109/TPAMI.2005.201
Bhandarkar SChen F(2005)Similarity Analysis of Video Sequences Using an Artificial Neural NetworkApplied Intelligence10.1007/s10791-005-6622-322:3(251-275)Online publication date: 1-May-2005
https://dl.acm.org/doi/10.1007/s10791-005-6622-3

Abstract

References

Cited By

Index Terms

Recommendations

Content-based multimedia retrieval in the presence of unknown user preferences

A Survey on Content-Based Retrieval for Multimedia Databases

Relevance feature mapping for content-based multimedia information retrieval

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Share

Share this Publication link

Share on social media

Affiliations