We focus on kernel-based smoothing estimators and propose a procedure that simultaneously adapts the level of smoothing required for density estimation and identifies the groups associated with the estimated density. The method relies on an EM-style algorithm with two maximization steps: one for the leave-one-out cross-validation maximum likelihood estimation of the vector of smoothing parameters and another for detecting the modes of the estimated density. An application to image segmentation is illustrated.