Distributed Dynamic Mean Shift Algorithm for Image Segmentation

Abstract

A fast and memory-efficient method has been created for the dynamic mean shift(DMS) algorithm,which is an iterative mode-seeking algorithm.Running the standard DMS algorithm requires a large amount of memory because the algorithm dynamically updates all data during iterations.Therefore,it is difficult to use a conventional DMS algorithm for clustering large dataset.This difficulty is overcome by partitioning a dataset into subsets,and the resultant procedure is called a “distributed DMS algorithm”.Experimental results on image segmentation show that the distributed DMS algorithm requires less memory than that of the conventionally used DMS algorithm.