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

Data-Driven Automatic Generation of Decision Tree for Motion Retrieval with Temporal-Spatial Features

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)
Jian Xiang ; Coll. of Comput. Sci., Zhejiang Univ., Hangzhou ; Yue-ting Zhuang ; Fei Wu

Along with the development of motion capture technique, more and more 3D motion libraries become available. In this paper, a novel approach is presented for motion retrieval based on data-driven decision tree with 3D temporal-spatial features. First 3D temporal-spatial features of each human joint are extracted with the help of keyspace. Since the gotten features of each joint are independent, data-driven decision tree is automatically constructed to reflect the influence of each point during the comparison of motion similarity. Experiment results show that the approaches are effective for motion data retrieval

Published in:

Machine Learning and Cybernetics, 2006 International Conference on

Date of Conference:

13-16 Aug. 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.