Explaining the AdaBoost Algorithms to non-technical people

Question

I\'ve been trying to understand the AdaBoost algorithm without much success. I\'m struggling with understanding the Viola Jones paper on Face Detection as an example.

Ca

Answer 1

AdaBoost uses a number of training sample images (such as faces) to pick a number of good 'features'/'classifiers'. For face recognition a classifiers is typically just a rectangle of pixels that has a certain average color value and a relative size. AdaBoost will look at a number of classifiers and find out which one is the best predictor of a face based on the sample images. After it has chosen the best classifier it will continue to find another and another until some threshold is reached and those classifiers combined together will provide the end result.

This part you may not want to share with non-technical people :) but it is interesting anyway. There are several mathematical tricks which make AdaBoost fast for face recognition such as the ability to add up all the color values of an image and store them in a 2 dimensional array so that the value in any position will be the sum of all the pixels up and to the left of that position. This array can be used to very quickly calculate the average color value of any rectangle within the image by subtracting the value found in the top left corner from the value found in the bottom right corner and dividing by the number of pixels in the rectangle. Using this trick you can quickly scan over an entire image looking for rectangles of different relative sizes that match or are close to a particular color.

Hope this helps.