# Accelerated Convergence using Dynamic Mean Shift

###
Kai Zhang, Jamesk T. Kwok, Ming Tang

**Abstract:**
Mean shift is an iterative mode-seeking algorithm
widely used in pattern recognition and computer vision. However,
its convergence is sometimes too slow to be practical. In this paper,
we improve the convergence speed of
mean shift by dynamically updating the sample set
during the iterations, and the resultant procedure is called {\em
dynamic\/} mean shift (DMS). When the data is locally Gaussian, it can
be shown that both the standard and dynamic mean shift algorithms
converge to the same optimal solution. However, while standard
mean shift only has linear convergence, the dynamic mean shift
algorithm has superlinear convergence.
%At the same time, this dynamic
%procedure gradually shrinks the sample set. Hence, even with the
%use of a fixed bandwidth, it achieves the same effect as the
%variable bandwidth (standard) mean shift procedure.
Experiments on
color image segmentation show that dynamic mean shift produces
comparable results as the standard mean shift algorithm, but can
significantly reduce the number of iterations for convergence and
takes much less time.
*Proceedings of the European Conference on Computer Vision
(ECCV'2006*),
pp.257-268,
Graz, Austria, 2006.

Pdf:
http://www.cs.ust.hk/~jamesk/papers/eccv06.pdf

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