By Topic

Relevance feedback for semantics 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

2 Author(s)
Janghyun Yoon ; Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA ; Jayant, N.

Content based image retrieval is one of the most active research areas in the field of multimedia technology. Currently, the relevance feedback approach has attracted great attention since it can bridge the gap between low-level features and the semantics of images. We propose a new relevance feedback technique, which uses the normal mixture model for the high-level similarity metric of the user's intention and estimates the unknown parameters from the user's feedback. Our approach is based on a novel hybrid algorithm where the criterion for the selection of the display image set is evolved from the most informative to the most probable as the retrieval process progresses. Experiments on the Corel image set show that the proposed algorithm outperforms MindReader at the semantics based search

Published in:

Image Processing, 2001. Proceedings. 2001 International Conference on  (Volume:1 )

Date of Conference: