Learning to detect low-level features

We introduce a method to detect low-level features that is prescriptive (as Canny edge-detection is) but trained by a user. The user simply chooses feature classes and points to class instances. Given a input image, we compute a probability map that indicates how likely it is to belong to each of the userdefined classes; so combining different kinds of feature detector into a single, user-trainable system. This paper explains how we characterise features, how we train and detect, and gives an algorithm that automatically determines feature scale. We empirically compare the method to standard edge and corner detectors, showing a measurable advantage in each case.