A robust kernel density estimator based mean-shift algorithm

We propose a robustification of the mean-shift algorithm. We understand robustness in the statistical sense as the deviation from the nominal, distributional assumption. The derivation of the robust mean-shift vector is based on a robust version of the kernel density estimator (KDE), where the KDE is interpreted as an inner product in a higher dimensional feature space. The mean in this formulation is replaced by an Ivies timate in order to robustify against outlying data points. We show the superiority of our algorithm compared to the standard mean-shift algorithm and to the median-shift algorithm using both simulated and real data in both contaminated and uncontaminated data. The real data stems from an image segmentation application for blood glucose measurement.