S-AdaBoost and pattern detection in complex environment

Jimmy Liu Jiang, Kia Fock Loe

Research output: Journal PublicationConference articlepeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)I/413-I/418
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
Publication statusPublished - 2003
Externally publishedYes
Event2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Madison, WI, United States
Duration: 18 Jun 200320 Jun 2003

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'S-AdaBoost and pattern detection in complex environment'. Together they form a unique fingerprint.

Cite this