Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode

A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data

A new method for real-time tracking of non-rigid objects seen from a moving camera isproposed. The central computational module is based on the mean shift iterations and nds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution

A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven

– theory & applications – mean-shift object tracking

The mean-shift algorithm is an efficient technique for track-ing 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing this scale or updating it while tracking blobs that are changing in size. In this pa

Abstract. Mean shift is a nonparametric clustering technique that does not require the number of clusters in input and can find clusters of ar-bitrary shapes. While appealing, the performance of the mean shift al-gorithm is sensitive to the selection of the bandwidth, and can fail to capture

Abstract—We build on the current understanding of mean shift as an optimization procedure. We demonstrate that, in the case of piecewise constant kernels, mean shift is equivalent to Newton’s method. Further, we prove that, for all kernels, the mean shift procedure is a quadratic bound maximization

Mean shift represents a general non-parametric mode finding/clustering procedure. In contrast to the classic K-means clustering approach (Duda, Hart & Stork, 2001), there are no embedded assumptions on the shape of the distribution nor the number of modes/clusters. Mean shift was first proposed

A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven