ADABOOST(Adaptative Boosting) Adaboost algorithm works by sequentially training a series of weak learners which are then combined into a single strong classifier. Each weak learner (classifier) attempts to minimize classification error on a particular distribution of the training data. Adaboost is used to boost the classification performance of simple “weak” learning algorithms. The boosted combination of classifiers not only minimizes the error on the training data but also leads to a minimized bound on the error of the test set.

AdaBoost Algorithm

AdaBoost->Perceptron  Binary Inputs: hi(x) = {0,1}  1 h1(x) 2 H(x) h2(x) 3 h3(x) T hT(x)

AdaBoost-Perceptron Image to classify h1 h2 h1 Garden Example perfect, it has the 2 weak classifiers 1 Not Garden  Very hIgh  low h2 1

Changes Sub-Windows Feature Pixel Feature-Column Feature-High Feature-Row Feature –Channel (color) Sign Function Feature-High Feature-Width Feature-Type Feature-Threshold (Area Difference) Pixel-Column Pixel-Row Pixel –Channel (color) Pixel-Threshold Sign Function

Integral Image A new image representation called Integral Image that allows for very fast feature evaluation. With it , we can compute features very rapidly at many scales. The integral Image at location x, y contains the sum of the pixels above and to the left of x,y: (x,y) Area (ABCD)= ii(A) + ii(B) - ii(C) - ii(D) B C D A

Scaling Important: Normalize the area difference The final detector is scanned across the image at multiples scales and locations. Scaling is achieved by scaling the detector itself, rather than scaling the image Important: Normalize the area difference 

Cascade Detector Constructing a cascade of classifiers which achieves increased detection performance while reducing computation time. Simpler classifiers are used to reject the majority of sub-windows, before more complex classifiers are called upon to achieve low false positives. A positive result from the first classifier triggers the evaluation to a second classifier which has also been adjusted to achieve very high detection rates….and so on.  High, Low false positives  Low, High false positives Classifier set 1 Classifier set 3 True True True “correct” decision All sub-windows False False False Reject sub-windows