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.


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