Cart (Loading....) | Create Account
Close category search window

LDA Versus MMD Approximation on Mislabeled Images for Dependant Selection of Visual Features and Their Heterogeneity

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)
Tollari, S. ; Lab. of Syst. & Inf. Sci., Univ. du Sud Toulon-Var, La Garde ; Glotin, H.

We propose first to generate new visual features based on entropy measure (heterogeneity), and then we address the question of feature selection in the context of mislabeled images for automatic image classification. We compare two methods of word dependant feature selection on mislabeled images: approximation of linear discriminant analysis (ALDA) and approximation of maximum marginal diversity (AMMD). A hierarchical ascendant classification (HAC) is trained and tested using full or reduced visual space. Experiments are conducted on 10 K Corel images with 52 keywords, 40 visual features (U) and 40 new heterogeneity features (H). Compared to HAC on all U features, we measure a classification gain of 56% and in the same time a reduction of 92% of the number of features using a simple late fusion of U and H

Published in:

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on  (Volume:2 )

Date of Conference:

14-19 May 2006

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.