Please use this identifier to cite or link to this item:
Title: S-AdaBoost and pattern detection in complex environment
Authors: Jiang, J.L. 
Loe, K.-F. 
Issue Date: 2003
Citation: Jiang, J.L.,Loe, K.-F. (2003). S-AdaBoost and pattern detection in complex environment. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1 : I/413-I/418. ScholarBank@NUS Repository.
Abstract: S-AdaBoost is a new variant of AdaBoost and is more effective than the conventional AdaBoost in handling outliers in pattern detection and classification in real world complex environment. Utilizing the Divide and Conquer Principle, S-AdaBoost divides the input space into a few sub-spaces and uses dedicated classifiers to classify patterns in the sub-spaces. The final classification result is the combination of the outputs of the dedicated classifiers. S-AdaBoost system is made up of an AdaBoost divider, an AdaBoost classifier, a dedicated classifier for outliers, and a non-linear combiner. In addition to presenting face detection test results in a complex airport environment, we have also conducted experiments on a number of benchmark databases to test the algorithm. The experiment results clearly show S-AdaBoost's effectiveness in pattern detection and classification.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN: 10636919
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Jan 26, 2023

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.