Hierarchical evolving mean-shift
Milan Šurkala, Karel Mozdřeň, Radovan Fusek, Eduard Sojka · 2012
Segmentation and filtration are widely discussed problems in image processing. Mean shift and its variants belong to the most popular methods in this area. In this paper, we propose a new variant of relatively new evolving mean shift that is based on the idea of minimization of dataset energy given by the sum of sizes of the mean-shift vectors. Our hierarchical EMS is focused on a significant reduction of computational time due to hierarchical evolution of the size of the kernel. We also present acceleration of precise point selection and vector recalculation, which can be applied also to original EMS.