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

Multiple descent cost competition: restorable self-organization and multimedia information processing

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

1 Author(s)
Matsuyama, Y. ; Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan

Multiple descent cost competition is a composition of learning phases for minimizing a given measure of total performance, i.e., cost. In the first phase of descent cost learning, elements of source data are grouped. Simultaneously, a weight vector for minimal learning, (a winner), is found. Then, the winner and its partners are updated for further cost reduction. Therefore, two classes of self-organizing feature maps are generated: a grouping feature map, and an ordinary weight vector feature map. The grouping feature map, together with the winners, retains most of the source data information. This feature map is able to assist in a high quality approximation of the original data. In the paper, the total algorithm of the multiple descent cost competition is explained and image processing concepts are introduced. A still image is first data-compressed, then a restored image is morphed using the grouping feature map by receiving directions given by an external intelligence. Next, an interpolation of frames is applied in order to complete animation coding. Examples of multimedia processing on virtual digital movies are given

Published in:

Neural Networks, IEEE Transactions on  (Volume:9 ,  Issue: 1 )

Date of Publication:

Jan 1998

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.