By Topic

Modeling Images With Multiple Trace Transforms for Pattern Analysis

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)
Nan Liu ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; Han Wang

Taking advantage of the various available trace transforms generated from a single image, the multiple trace feature (MTF) is proposed as a new image representation. In the process of MTF construction, genetic algorithms (GAs) play a key role as an information fusion tool. The systematic evaluations on a combo face data set comprising ORL, Yale, and UMIST databases reveal that MTF presents high discriminative power in terms of outperforming features extracted from principal component analysis (PCA) and linear discriminant analysis (LDA). In addition, the proposed Bagging-based extension of fitness guides GAs achieving more fitting features for classification.

Published in:

Signal Processing Letters, IEEE  (Volume:16 ,  Issue: 5 )