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

Handling label noise in video classification via multiple instance learning

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)
Leung, T. ; Google Inc., Mountain View, CA, USA ; Yang Song ; Zhang, J.

In many classification tasks, the use of expert-labeled data for training is often prohibitively expensive. The use of weakly-labeled data is an attractive solution but raises the problem of label noise. Multiple instance learning, whereby training samples are “bagged” instead of treated as singletons, offers a possible approach to mitigating the effects of label noise. In this paper, we propose the use of MILBoost [28] in a large-scale video taxonomic classification system comprised of hundreds of binary classifiers to handle noisy training data. We test on data with both artificial and real-world noise and compare against the state-of-the-art classifiers based on AdaBoost. We also explore the effects of different bag sizes on different levels of noise on the final classifier performance. Experiments show that when training classifiers with noisy data, MILBoost provides an improvement in performance.

Published in:

Computer Vision (ICCV), 2011 IEEE International Conference on

Date of Conference:

6-13 Nov. 2011

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.