Name
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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/CVPR.2003.1211383 | CVPR (1) |
Keywords | Field | DocType |
complex airport environment,s-adaboost system,dedicated classifier,final classification result,complex environment,adaboost divider,adaboost classifier,face detection test result,pattern detection,conventional adaboost,boosting,pattern analysis,divide and conquer,algorithm design and analysis,face detection,face recognition,image classification,benchmark testing,polynomials,feature extraction | Facial recognition system,AdaBoost,Object-class detection,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Divide and conquer algorithms,Face detection,Classifier (linguistics),Contextual image classification,Machine learning | Conference |
ISSN | ISBN | Citations |
1063-6919 | 0-7695-1900-8 | 9 |
PageRank | References | Authors |
0.64 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jimmy Liu Jiang | 1 | 9 | 1.66 |
| Kia-Fock Loe | 2 | 180 | 20.88 |