21e colloque GRETSI sur le traitement du signal et des images 11-14 septembre 2007, Troyes, France www.gretsi2007.org

Abstract This paper presents a pedestrian detection method based on the multiple kernel framework. One main problematic of pattern recognition resides in the pertinent characterization of the data. Depending on the descriptor, we sometimes have to tune the descriptor in order to be more efficient. Instead of accomplishing this tuning manually by testing and comparing all possible values we propose here to use the multiple kernel framework. The aim is to use a kernel as a linear combination of different kernels in order to combine and select automatically the best kernels within a set of kernels. This can be assimilated as model selection, where one kernel of the set corresponds to one model. We first introduce the MKL framework and finally apply this approach for a parameter tuning task and a feature selection problem.