This paper investigates the scale selection problem for vector-valued, nonlinear diffusion scale-spaces. We present a new approach for the localization scale selection, which aims at maximizing the image content's presence by finding the scale that has a maximum correlation with the noise-free image. For scale-space discretization, we propose to adapt the optimal diffusion stopping time criterion of Mrazek and Navara in such a way that it may identify multiple scales of importance.