By Topic

Relevance vector machine for content-based retrieval of 3D head models

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

4 Author(s)
Pui Fong Yeung ; Dept. of Comput. Sci., Hong Kong City Univ., China ; Hau San Wong ; Bo Ma ; Ip, H.H.S.

In this paper, we propose a novel 3D head model retrieval approach in which the queries are 2D face views instead of less readily available 3D head models. The basic idea is to characterize the corresponding relations between 2D view feature and 3D model feature based on a machine learning approach. Thus the subsequent feature matching can be carried out in 3D feature space. As an effective solution to regression problems, relevance vector machine is used in this paper to establish an association between 2D and 3D features. Experimental results show that our proposed 2D query based method is comparable with the direct 3D query based one.

Published in:

Information Visualisation, 2005. Proceedings. Ninth International Conference on

Date of Conference:

6-8 July 2005