Unsupervised machine learning – clustering, PCA, and eigenfaces

In this section, we will discuss a few popular machine learning algorithms along with their applications in image processing. Let's start with a couple of clustering algorithms and their applications in color quantization and the segmentation of images. We will use the scikit-learn library's implementation for these clustering algorithms.

K-means clustering for image segmentation with color quantization

In this section, we will demonstrate how to perform a pixel-wise Vector Quantization (VQ) of the pepper image, reducing the number of colors required to show the image from 250 unique colors down to four colors, while preserving the overall appearance quality. In this example, pixels are represented in a 3D space and k-means is used to find four color clusters.

In image processing literature, the codebook is obtained from k-means (the cluster centers) and is called the color palette. In a color palette, using a single byte, up to...