Stochastic gradient kernel density mode-seeking

As a well known fixed-point iteration algorithm for kernel density mode-seeking, mean-shift has attracted wide attention in pattern recognition field. To date, mean-shift algorithm is typically implemented in a batch way with the entire data set known at once. In this paper, based on stochastic gradient optimization technique, we present the stochastic gradient mean-shift (SG-MS) along with its approximation performance analysis. We apply SG-MS to the speedup of Gaussian blurring mean-shift (GBMS) clustering. Experiments in toy problems and image segmentation show that, while the clustering accuracy is comparable between SG-GBMS and Naive-GBMS, the former significantly outperforms the latter in running time.