By Topic

OFS: A Feature Selection Method for Shape-based 3D Model Retrieval

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)
Fan Yang ; Beihang Univ., Beijing ; Biao Leng

We focus on improving the effectiveness of shape-based similarity retrieval in 3D model repositories. Motivated by retrieval performance of several individual 3D model descriptors for projected images in shape-based approaches, we present an optimized feature selection (OFS) method to choose a perfect feature vector based on each query model. Experimental results show that the OFS method for shape-based 3D model retrieval has achieved significant improvements on retrieval effectiveness of 3D shape search with several measures on a standard 3D database, and it provides a retrieval performance 45.5% better than the average precision of several descriptors. Compared to the currently best method light field descriptor (LFD), OFS has better retrieval effectiveness. Furthermore, the feature vector components of our approach are only 6.77% of that in LFD.

Published in:

Computer-Aided Design and Computer Graphics, 2007 10th IEEE International Conference on

Date of Conference:

15-18 Oct. 2007