I actually really liked this paper :) I've read a number of image retrieval/object detection papers, and I found the methods presented in this paper to be some of the most interesting.

In particular, I really like the idea of using boosting for feature selection (even though this is actually presented in the earlier paper). Being a big machine learning fan, I think it's great to learn what the best features are instead of trying to decide statically.

I also thought the idea of cascading classifiers was good. This had a number of nice effects. First, I think it allowed the detection performance to be increased by cascading the classifiers. I also think, however, that this hits on another key point of the paper which is temporal performance. The paper made specific compomises and design decisions to get the system so that it could run fairly well in real time. This is quite different from many other systems.

As for the features, they are simply those 24x24 rectangular things seen at the top right of page two. Since the paper only deals with black and white (i.e. intensities) a simple difference of sums is taken from the light and dark regions. I think the motivation is similar to that of earlier work in Tieu and Viola (2000), Boosting Image Retrieval, which I know some of us read for cs254 in the Spring.