Edited by author 10-25-2001 12:39 PM
As far as I can see the major difference in this implementation of AdaBoost compared to the standard ones is that instead of training the same classifier on each training round (with differently weighted data) the Viola & Jones version trains an individual classifier for each feature and then selects the one with the smallest error (with respect to the weight distribution). I'm wondering whether this modification changes in any way the theoretical performance guarantees presented in the AdaBoost papers we read earlier. On second though, it probably doesn't since the weak classifier in AdaBoost can be any non-random classifier including the really weak single feature based classifier used here. A second comment is on redundancy in the feature calculations. Since the actual test images are scanned by moving a window one pixel a time it would seem like there is a lot of overlap in calculating the integral image features. Is this true and would there be a way to speed up the calculation even further by storing some of the features calculated in the previous window.

So much for semi-intelligent comments and on to ranting about the paper. Because there was quite a bit of stuff included in the paper I had a really hard time in putting together how the whole classifier (with the cascades and all) would actually work. The authors didn't seem to make much effort in trying to make it easy for other people to implement their method (good work Ian!). Also, what's up with reference [2] in the paper. And as a final comment judging from Fig. 10 in the paper a much easier task for object recognition would be recognizing soccer balls from cluttered images. I'm not so sure of the commercial potential of a rapid soccer ball detector though ...