Cart (Loading....) | Create Account
Close category search window
 

Content-based image retrieval system with new low-level features, new similarity metric, and novel feedback learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Xue-Long Li ; Inf. Process. Center, Univ. of Sci. & Technol. of China, Hefei, China

A currently relevant research field in computer science is the management of multimedia databases. Two related key issues are achieving an efficient content-based retrieval and a fast response time. Relevance feedback is a powerful tool to improve the retrieval results of the CBIR systems. However the traditional relevance feedback could only search a small feature subspace comparing with the entire huge feature space. So, this paper provides solutions to enlarge the searching feature subspace in a CBIR system, effectively. Firstly, an adaptive system query strategy to user behaviors is introduced to improve the performance of relevance feedback. Then a feedback scheme based on multi-features classification is developed. Both tactics enlarge the searching feature subspace efficiently. From experimental results, it is clear that the feedback scheme has a much better performance than the traditional CBIR systems. To develop such a scheme, a new effective texture feature and an efficient way to measure the dis-similarity between two image features are proposed in the CBIR system, which provides solutions to a general query on a large image database with 56,600 images.

Published in:

Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on  (Volume:2 )

Date of Conference:

2002

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.