On the Evolution of Interest Operators using Genetic Programming

Interest operators play an important role in computer vision. Depending on the type of the environment some features may prove to be more advantageous than others. Thus detection of interesting features has to be made adaptive such that the best features according to some measure are extracted. We are trying to evolve such feature detectors using genetic programming. In this paper we describe our results where the desired operator, which is a Moravec interest operator, is directly specified. We show that the problem is a rather difficult one. Only an approximation to the Moravec operator could be evolved using several sets of elementary functions.