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.

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Xiaotong Yuan, Stan Z. Li
• Keywords: Computer Vision, CVPR 2009, Kernel Density, Stochastic Gradient