Pattern Recognition
Neural Computation
Mode-Finding for Mixtures of Gaussian Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Is a Bound Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Slow Convergence of EM and VBEM in Low-Noise Linear Models
Neural Computation
Fast nonparametric clustering with Gaussian blurring mean-shift
ICML '06 Proceedings of the 23rd international conference on Machine learning
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
On the number of modes of a Gaussian mixture
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Scale-based clustering using the radial basis function network
IEEE Transactions on Neural Networks
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Fast nonparametric clustering with Gaussian blurring mean-shift
ICML '06 Proceedings of the 23rd international conference on Machine learning
Averaging Centerlines: Mean Shift on Paths
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Mean shift: An information theoretic perspective
Pattern Recognition Letters
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Nonlinear Mean Shift over Riemannian Manifolds
International Journal of Computer Vision
On the effectiveness of multiscale mode filters in edge preserving image filtering
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
Sequential minimal optimization in convex clustering repetitions
Statistical Analysis and Data Mining
Dynamics of a mean-shift-like algorithm and its applications on clustering
Information Processing Letters
An analysis of a spatial EA parallel boosting algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
On the convergence of the mean shift algorithm in the one-dimensional space
Pattern Recognition Letters
A generative model and a generalized trust region Newton method for noise reduction
Computational Optimization and Applications
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The mean-shift algorithm, based on ideas proposed by Fukunaga and Hostetler [16], is a hill-climbing algorithm on the density defined by a finite mixture or a kernel density estimate. Mean-shift can be used as a nonparametric clustering method and has attracted recent attention in computer vision applications such as image segmentation or tracking. We show that, when the kernel is Gaussian, mean-shift is an expectation-maximization (EM) algorithm and, when the kernel is non-Gaussian, mean-shift is a generalized EM algorithm. This implies that mean-shift converges from almost any starting point and that, in general, its convergence is of linear order. For Gaussian mean-shift, we show: 1) the rate of linear convergence approaches 0 (superlinear convergence) for very narrow or very wide kernels, but is often close to 1 (thus, extremely slow) for intermediate widths and exactly 1 (sublinear convergence) for widths at which modes merge, 2) the iterates approach the mode along the local principal component of the data points from the inside of the convex hull of the data points, and 3) the convergence domains are nonconvex and can be disconnected and show fractal behavior. We suggest ways of accelerating mean-shift based on the EM interpretation.