By Topic

Geometric Structure Based Image Clustering and Image Matching

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
$33 $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)
Sulan Zhang ; Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 100080; Graduate School of the Chinese Academy of Sciences, 100039, ; Chunqi Shi ; Zhiyong Zhang ; Zhongzhi Shi

We propose two geometric structure based approaches GGCI (global geometric clustering for image) and GSIM (geometric structure based image matching) for image clustering and image matching, respectively. For face images or object images taken with varying factors, the GGCI approach learns the global geometric structure of images space and clusters images based on geodesic distance instead of Euclidean distance and the extended nearest neighbor approach. The GSIM approach uses the minimal Euclidean distance between parts of image and the pattern and its variations as matching criteria and threshold strategy for image matching. We demonstrate experimentally that the GGCI approach achieves lower error rates and the GSIM approach brings down the sensitivity of gray values to change in radiometry and reduces multi local extrema to some extent

Published in:

2006 5th IEEE International Conference on Cognitive Informatics  (Volume:1 )

Date of Conference:

17-19 July 2006