By Topic

Content based image retrieval using Multiple Instance Decision Based Neural Networks

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)
Yeong-Yuh Xu ; Dept. of Comput. Sci. & Inf. Eng., Hungkuang Univ., Taichung, Taiwan ; Chi-Huang Shih

This paper presents a Multiple-Instance Decision Based Neural Networks (MI-DBNN) based image retrieval system. Without precisely image segmentation, the image retrieval problem is considered as a Multiple-Instance Learning problem. A set of exemplar images are selected, each of which is labelled as conceptual related (positive) or conceptual unrelated (negative) image. Then, the MI-DBNN is trained to learn the user's preferred image concept from the positive and negative examples. The proposed system is built and located on Experimental results show that our method can significantly improve the retrieving performance from 68.4% to 80.7%, which outperforms to the results of some leading image retrieval methods.

Published in:

Computational Intelligence and Cybernetics (CyberneticsCom), 2012 IEEE International Conference on

Date of Conference:

12-14 July 2012