1. 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.

2. AdaBoost Algorithm

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

4. 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

5. 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

6. 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

7. 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 

8. 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