视频监控与视频分析-第十一章-随机森林与ADABOOST.ppt

Scaling Integral image enables us to evaluate all rectangle sizes in constant time. Therefore, no image scaling is necessary. Scale the rectangular features instead! 1 2 3 4 5 6 Boosting Boosting is a classification scheme that works by combining weak learners into a more accurate ensemble classifier A weak learner need only do better than chance Training consists of multiple boosting rounds During each boosting round, we select a weak learner that does well on examples that were hard for the previous weak learners “Hardness” is captured by weights attached to training examples Y. Freund and R. Schapire, A short introduction to boosting, Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September, 1999. The AdaBoost Algorithm Given: Initialization: For : Find classifier which minimizes error wrt Dt ,i.e., Weight classifier: Update distribution: The AdaBoost Algorithm Given: Initialization: For : Find classifier which minimizes error wrt Dt ,i.e., Weight classifier: Update distribution: Output final classifier: Weak Learners for Face Detection Given: Initialization: For : Find classifier which minimizes error wrt Dt ,i.e., Weight classifier: Update distribution: Output final classifier: What base learner is proper for face detection? Weak Learners for Face Detection window value of rectangle feature parity threshold Boosting Training set contains face and nonface examples Initially, with equal weight For each round of boosting: Evaluate each rectangle filter on each example Select best threshold for each filter Select best filter/threshold combination Reweight examples Computational complexity of learning: O(MNK) M rounds, N examples, K features Features Selected by Boosting First two features selected by boosting: This feature combination can yield 100% detection rate and 50% false positive rate ROC Cur