Weak vs Strong descriptors - machine vision

Question

I have heard of Haar-like features being described as weak descriptors and that the Adaboost method is advantageous over SVM in this case because of this. My question is what is a weak descriptor and strong descriptor and why does a boosting method perform better than an SVM (for example)?

Answer 1

A weak descriptor would be something which is not too refined or tuned (eg: haar features, edge maps etc). A strong descriptor(SIFT/SURF/MSER) would be something which is accurate, has high repeatability under blur, viewpoint/illumination change, JPEG compression. A boosting method would perform better for weak descriptors and SVM would be suitable for a strong descriptor. This is because the idea of boosting is to combine a lot of weak classifiers to learn a classifier. In the case of haar like features, adaboost combines many such weak features to learn a strong classifier. SVM tries to fit a hyperplane between the most confusing features between the two classes, so for SVM to perform better, the confusion between the classes should be less and features should be robust and accurate.