A review of mean-shift algorithms for clustering

TL;DR

This paper reviews mean-shift clustering algorithms, covering their theory, variants, extensions, and applications in image segmentation, denoising, and regression, highlighting their nonparametric density-based approach.

Contribution

It provides a comprehensive overview of mean-shift algorithms, including recent extensions and practical acceleration strategies for large datasets.

Findings

Mean-shift algorithms effectively identify high-density regions in data.

Extensions like K-modes and Laplacian K-modes improve clustering flexibility.

Applications demonstrate effectiveness in image segmentation and manifold denoising.

Abstract

A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found using mean-shift algorithms. We describe the theory and practice behind clustering based on kernel density estimates and mean-shift algorithms. We discuss the blurring and non-blurring versions of mean-shift; theoretical results about mean-shift algorithms and Gaussian mixtures; relations with scale-space theory, spectral clustering and other algorithms; extensions to tracking, to manifold and graph data, and to manifold denoising; K-modes and Laplacian K-modes algorithms; acceleration strategies for large datasets; and applications to image segmentation, manifold denoising and multivalued regression.