Design of Blurring Mean-Shift Algorithms for Data Classification

Carlo Grillenzoni ()
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Carlo Grillenzoni: IUAV University

Journal of Classification, 2016, vol. 33, issue 2, No 6, 262-281

Abstract: Abstract The mean-shift algorithm is an iterative method of mode seeking and data clustering based on the kernel density estimator. The blurring mean-shift is an accelerated version which uses the original data only in the first step, then re-smoothes previous estimates. It converges to local centroids, but may suffer from problems of asymptotic bias, which fundamentally depend on the design of its smoothing components. This paper develops nearest-neighbor implementations and data-driven techniques of bandwidth selection, which enhance the clustering performance of the blurring method. These solutions can be applied to the whole class of mean-shift algorithms, including the iterative local mean method. Extended simulation experiments and applications to well known data-sets show the goodness of the blurring estimator with respect to other algorithms.

Keywords: Bandwidth selection; Cluster stability; Kernel density; Image segmentation; Local means; Monotone convergence; Nearest neighbors (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00357-016-9205-7

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