What is Mean Shift Segmentation?
Mean Shift is an unsupervised clustering algorithm used in computer vision and image processing. Unlike K-Means, you don’t need to tell it how many clusters to create. Instead, it automatically discovers “modes” (peaks) in the data distribution. For images, this means pixels with similar colors and locations are grouped together, producing smooth regions while preserving important boundaries. The result looks artistic and natural — almost like your photo has been painted with a clean brush stroke.
Why Use Mean Shift?
- Artistic Effects: Create painterly, smooth artistic effects while keeping edges sharp.
- Noise Reduction: Simplify noisy photos into clean colour regions.
- Smart Clustering: Automatically find patterns without guessing the number of clusters.
Before and After Example
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How It Works (Simple Explanation)
Imagine placing a window (like a magnifying glass) over part of the image. You calculate the average colour and location of the pixels inside it. Then you “shift” the window toward this average. This process repeats until the window stops moving — meaning you’ve found a cluster of similar pixels. Each cluster forms a region in the final image.
The Mathematics Behind Mean Shift
The core idea of Mean Shift is finding modes in a probability density function without assuming its shape. Given a set of points x₁, x₂, …, xₙ, we estimate the density using a kernel function K:
Here, h is the bandwidth (like the radius of the search window), and d is the dimension. The Mean Shift vector is calculated as:
At each step, the point x is updated to the weighted mean of its neighbors. When the Mean Shift vector m(x) becomes very small, you’ve reached a mode (cluster center).
Quick Tips for Parameters
- Color Radius: Higher numbers = stronger color merging (more abstract).
- Spatial Radius: Higher numbers = larger, smoother regions.
- Pyramid Level: Use higher levels (up to 8) for faster processing, though you may lose some fine detail.