By default, EuclideanDistance is used:

Specify the distance function:

Use a custom distance function:

By default, only one iteration of mean shift is applied to input:

Use MaxIterations to specify the number of iterations:

Use MaxIterations->Infinity to run until convergence:

Use MaxIterations to iteratively smooth data:

By default, zero tolerance is used:

Specify the accepted tolerance:

By default, uniform weights are used:

Weigh neighbors linearly, based on their distance from each sample:

Weigh neighbors exponentially, based on their squared distance from each sample:

By "Gaussian" weights for color segmentation:

With default uniform weights:

Find large-scale features in a noisy dataset:

Image smoothing using mean shift of 5D features in the joint spatial-range domain:

Use LABColor values, suitable for computing color distances:

Compute spatial location features normalized to the range 0 to 1:

Construct 5D features from color and location of pixels:

Perform one iteration of mean-shift filtering using "Gaussian" weights and EuclideanDistance, which is equivalent to multiplying Gaussian-weighted spatial and range features:

Create the output image using the filtered color values:

Perform 10 iterations:

Compute a 1D mean-shift vector:

Generate unimodal 2D data:

Compute the trajectory of a point as it iteratively ascends toward the mode:

Visualize the norm and direction of the successive mean-shift vectors:

Visualize the ascent:

Get sample data from some distribution with three modes: 0, 2, and 4:

Apply mean shift until convergence for samples that tessellates the data:

Prune points that are not local maxima of the underlying PDF by restarting mean shift from a perturbed position:

Find modes by gathering the nearby peaks:

Generate data from some distribution:

Apply mean shift until all data points have converged:

Gather the result into clusters:

Visualize the clustering:

Generate a large amount of data from some distribution:

Compute trajectories for a fraction of the data samples:

Merge paths that end within radius of each other:

Label each trajectory and compute the corresponding nearest function:

Each unlabeled sample is assigned the label of the closest trajectory:

Visualize the clustering:

Computing the mean shift until convergence for the entire dataset would take much longer:

Iterate mean-shift filtering in the 3D feature space of the "LAB" color components:

Perform mean-shift filtering in the 5D spatial-range feature domain:

Merge components whose feature values are apart by less than the radius:

Compute the final segments and quantized image: