Learning invariant region descriptor operators with genetic programming and the F-measure

Recognizing and localizing objects is a classical problem in computer vision that is an important stage for many automated systems. In order to perform object recognition many researchers have focused on local features as the basis of their proposed methodologies. This work is devoted to the task of learning invariant region descriptor operators with genetic programming. The idea is to find a set of expressions that could be equal or better than the weighted gradient magnitude that is normally applied on the SIFT descriptor. This magnitude corresponds to the operator that we would like to improve through genetic programming (GP). The key for a successful problem statement was achieved with the F-measure. After a bibliographical study we have found a criterion that is simple, reliable, and useful in the estimation of such a metric. The measure that we propose here is based on the harmonic mean which is normally used by the information retrieval community. Experimental results show that the evolved descriptor¿s operator can enhance significantly the overall performance of the SIFT descriptor and surpass other state-of-the-art algorithms.