By Topic

Locally Smooth Metric Learning with Application to 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)
Dit-Yan Yeung ; Hong Kong Univ. of Sci. & Technol., Kowloon ; Hong Chang

In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis distance, we define locally smooth metrics using local affine transformations which are more flexible. The data set after metric learning can preserve the original topological structures. Moreover, our method is fairly efficient and may be used as a preprocessing step for various subsequent learning tasks, including classification, clustering, and nonlinear dimensionality reduction. In particular, we demonstrate that our method can boost the performance of content-based image retrieval (CBIR) tasks. Experimental results provide empirical evidence for the effectiveness of our approach.

Published in:

Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on

Date of Conference:

14-21 Oct. 2007