Evolving mean shift with adaptive bandwidth: A fast and noise robust approach

Qi Zhao, Zhi Yang, Hai Tao, Wentai Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


This paper presents a novel nonparametric clustering algorithm called evolving mean shift (EMS) algorithm. The algorithm iteratively shrinks a dataset and generates well formed clusters in just a couple of iterations. An energy function is defined to characterize the compactness of a dataset and we prove that the energy converges to zero at an exponential rate. The EMS is insensitive to noise as it automatically handles noisy data at an early stage. The single but critical user parameter, i.e., the kernel bandwidth, of the mean shift clustering family is adaptively updated to accommodate the evolving data density and alleviate the contradiction between global and local features. The algorithm has been applied and tested with image segmentation and neural spike sorting, where the improved accuracy can be obtained at a much faster performance, as demonstrated both qualitatively and quantitatively.

Original languageEnglish (US)
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages11
EditionPART 1
StatePublished - 2010
Event9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China
Duration: Sep 23 2009Sep 27 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5994 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th Asian Conference on Computer Vision, ACCV 2009


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