Floatcascade learning for fast imbalanced web mining
Proceedings of the 17th international conference on World Wide Web
Asymmetric Learning for Pedestrian Detection Based on Joint Local Orientation Histograms
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Gabor-based dynamic representation for human fatigue monitoring in facial image sequences
Pattern Recognition Letters
Asymmetric totally-corrective boosting for real-time object detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
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Object detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR) , and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods.