By Topic

A Dynamic User Concept Pattern Learning Framework for Content-Based Image Retrieval

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

4 Author(s)
Shu-Ching Chen ; Sch. of Comput. Sci., Florida Int. Univ., Miami, FL ; Rubin, S.H. ; Mei-Ling Shyu ; Chengcui Zhang

A rapid increase in the amount of image data and the inefficiency of traditional text-based image retrieval systems have served to make content-based image retrieval an active research field. It is crucial to effectively discover users' concept patterns through an acquired understanding of the subjective role played by humans in the retrieval process for such systems. A learning and retrieval framework is used to achieve this. It seamlessly incorporates multiple instance learning for relevant feedback to discover users concept patterns-especially in the region of greatest user interest. It also maps the local feature vector of that region to the high-level concept pattern. This underlying mapping can be progressively discovered through feedback and learning. The user guides the retrieval systems learning process using his/her focus of attention. Retrieval performance is tested to establish the feasibility and effectiveness of the proposed learning and retrieval framework

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:36 ,  Issue: 6 )