I have an old exam question for my pattern recognition course which is stated as follows:
Consider a pool of Haar features, determine if these Haar features are independent or not. Is it a problem in boosting algorithms (e.g. AdaBoost)? Why (not)?
I have a really hard time figuring out an appropriate response to this question. One could argue that having dependent features is not desirable because then one of two classifiers does not really bring any new information or could possibly perform the same classification as the first one. The more independent features, the more independent, unique (weak) classifiers you have, but I don't know if this even makes sense.