Let's say we are doing logistic regression for classification. When the features are used directly, it means we are using some characteristics (features) of the object to classify them. But when using RBF (say with Gaussian kernel), we are using the similarity of an object with some prototype objects as features. But what is the intuition behind this? How does it help?