In this chapter, we discussed a few classical machine learning techniques and their applications in solving image processing problems. We started with unsupervised machine learning algorithms such as clustering and principal component analysis. We demonstrated k-means and spectral clustering algorithms with scikit-learn and showed you how they can be used in vector quantization and segmentation. Next, we saw how PCA can be used in dimension reduction and the visualization of high-dimensional datasets such as the scikit-learn handwritten digits images dataset. Also, how the PCA can be used to implement eigenfaces using a scikit-learn face dataset was illustrated.

Then, we discussed a few supervised machine learning classification models, such as kNN, the Gaussian Bayes generative model, and SVM to solve problems such as the classification of the handwritten digits dataset. Finally, we discussed a couple of classical machine learning techniques for object detection in images, namely Viola-Jones' AdaBoost cascade classifier with Haar-like features for face detection (and finding the most important features with random forest classifier) and HOG-SVM for pedestrian detection.

In the next chapter, we are going to start discussing recent advances in image processing with deep learning techniques.