Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Spectral Images and Features Co-Clustering with Application to 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

3 Author(s)
Jian Guan ; Sch. of Comput. Sci. & Inf. Technol., Nottingham Univ. ; Guoping Qiu ; Xiang-Yang Xue

In this paper, we present a spectral graph partitioning method for the co-clustering of images and features. We present experimental results, which show that spectral co-clustering has computational advantages over traditional k-means algorithm, especially when the dimensionalities of feature vectors are high. In the context of image clustering, we also show that spectral co-clustering gives better performances. We advocate that the images and features co-clustering framework offers new opportunities for developing advanced image database management technology and illustrate a possible scheme for exploiting the co-clustering results for developing a novel content-based image retrieval method

Published in:

Multimedia Signal Processing, 2005 IEEE 7th Workshop on

Date of Conference:

Oct. 30 2005-Nov. 2 2005