By Topic

Robust semantic concept detection in large video collections

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)
Jialie Shen ; Singapore Manage. Univ., Singapore, Singapore ; Dacheng Tao ; Xuelong Li

With explosive amounts of video data emerging from the Internet, automatic video concept detection is becoming very important and has been received great attention. However, reported approaches mainly suffer from low identification accuracy and poor robustness over different concepts. One of the main reason is that the existing approaches typically isolate the video signature generation from the process of classifier training. Also, very few approaches consider effects of multiple video features. The paper describes a novel approach fusing different information from diverse knowledge sources to facilitate effective video concept detection. The system is designed based on CM*F scheme and its basic architecture contains two core components including 1) CM*F based video signature generation scheme and 2) CM*F based video concept detector. To evaluate the approach proposed, an extensive experimental study on two large video databases has been carried out. The results demonstrate the superiority of the method in terms of effectiveness and robustness.

Published in:

Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on

Date of Conference:

11-14 Oct. 2009