By Topic

Weighted Subspace Distance and Its Applications to Object Recognition and Retrieval With Image Sets

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)
Fei Li ; Dept. of Autom., Tsinghua Univ., Beijing ; Qionghai Dai ; Wenli Xu ; Guihua Er

We address the problem of measuring the distance between two subspaces, each of which is spanned by an image set. In the existing methods, only the orthonormal basis is used to represent the subspace. However, the images are usually distributed in a limited area, rather than the whole subspace. Therefore, the characteristics of the distribution should also be considered. In this letter, a weighted subspace distance (WSD) is proposed, in which the principal component values of the data set are adopted to calculate the weights. Experimental results on object recognition and retrieval with image sets demonstrate the effectiveness of our proposal.

Published in:

Signal Processing Letters, IEEE  (Volume:16 ,  Issue: 3 )