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

Equal-average equal-variance nearest neighbor search algorithm based on Hadamard transform

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)
Zhe-Ming Lu ; Dept. of Autom. Test & Control, Harbin Inst. of Technol., China ; Hui Pei

A new fast nearest neighbor codeword search algorithm for image vector quantization (VQ) is introduced. This algorithm uses two significant features of a hadamard transformed vector, that is, the average, the variance, to eliminate more unmatched code words. It saves a great deal of computational time and distortion calculations. Experimental results demonstrate the performance of the proposed algorithm is good.

Published in:

Machine Learning and Cybernetics, 2003 International Conference on  (Volume:5 )

Date of Conference:

2-5 Nov. 2003

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.