By Topic

A boosting method based on SVM for relevance feedback in content-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

4 Author(s)
Tao Wei ; Dept. of Comput. Sci. & Tech., Tsinghua Univ., Beijing, China ; Zheng Qin ; Xiaoman Cao ; Biao Leng

The technique of relevance feedback has been introduced to content-based 3D model retrieval. Support Vector Machine as a learner is one of the classical approaches in relevance feedback. And the Boosting method, as one of the ensemble methods, can establish a strong leaner by combing the component learners. In this paper, a novel relevance feedback mechanism, which makes use of the main idea of boosting and the component SVM, is presented and applied to the content-based 3D model retrieval. The experiments, based on the 3D model database Princeton Shape Benchmark, show that the relevance feedback algorithm can improve the retrieval performance of traditional SVM in 3D model retrieval.

Published in:

Software Engineering and Data Mining (SEDM), 2010 2nd International Conference on

Date of Conference:

23-25 June 2010