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

Hierarchical overlapped growing neural gas networks with applications to video shot detection and motion characterization

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)
Xiang Cao ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Suganthan, P.N.

This paper describes a hierarchical overlapped architecture (HOGNG) based upon the growing neural gas (GNG) network. The proposed architecture combines the unsupervised and supervised learning schemes in GNG. This novel network model was used to perform automatic video shot detection and motion characterization. Experimental results are presented to show the good classification accuracy of the proposed algorithm on real MPEG video sequences

Published in:

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on  (Volume:2 )

Date of Conference:

2002

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.