paper critique &summarization : Rapid object detection using a boosted cascade of simple features

Title:Rapid object detection using a boosted cascade of simple features
Author: Paula Viola and Michael Jones

Summarization:
Adaboost is a useful tool which based on the concept that collect many "weak classify" will construct a "strong classify" that is solid. In every stage, it test all data and add the weight with the wrongly cases and reduce the weight with the collect cases, which want to minimize the training error. And this paper also uses a technique called "Integral image" to improve the calculation efficiency based on a simple addition and subtraction. Adaboost could use for feature selection, this paper use it for face detection, which could filter the non-face image one by one classifiier with important decrease, and it is also suitable for other features to select the valuable feature.

critique:
The idea of this paper is cool and simple, but it is a supervised algorithm, so the training data is important for the performance. Moreover, as a feature selection algorithm, it also select the most important feature easily, whcih could be useful and efficiency for approximate classify.