Discriminant Feature Selection by Genetic Programming: Towards a domain independent multi-class object detection system

In order to implement a multi-class object detection sys-tem, an efficient object representation is needed; in this short paper, we present a feature selection method based on the Genetic Programming paradigm. This method allows for the identification of a set of features that best represent the classes in the problem at hand. The idea would then be to have a broad set of features to describe any object, and then to use the presented feature selection method to adapt the description to the actual needs of the classification pro-blem. Furthermore, the tree like solutions generated by the method can be interpreted and modified for increased gen-erality. A brief review of literature, the first implementation of the method and the first results are presented here. The method shows potential to be used as a building block of a detection system, although further experimentation is under-way in order to fully asses the power of the method