Machine Learning with Swift

Image segmentation using k-means

The k-means algorithm was invented in the field of digital signal processing and is still in common use in that field for signal quantization. For this task, it performs much better than for pin clustering. Let's look at an example on the following diagram. The picture can be segmented into meaningful parts using color space quantization. We choose the number of clusters, then run k-means on every pixel's RGB values, and find the cluster's centroids. Then we replace each pixel with the color of its corresponding centroid. This can be used in image editing for separating objects from the background or for lossy image compression. In Chapter 12, Optimizing Neural Networks for Mobile Devices, we're going to use ...