We learned the classical feature-based approach to handling images in a machine learning context by reducing a million pixels to a few numeric dimensions. All the technologies that we learned in the other chapters suddenly become directly applicable to image problems. This includes classification, which is often referred to as pattern recognition when the inputs are images, clustering, or dimensionality reduction (even topic modeling can be performed on images, often with very interesting results).

We also learned how to use local features in a bag-of-words model for classification. This is a very modern approach to computer vision and achieves good results while being robust to many irrelevant aspects of the image, such as illumination and also uneven illumination in the same image. We also used clustering as a useful intermediate step in classification rather than as an end in itself.

We focused on mahotas, which is one of the major computer vision libraries in Python. There are others that are equally well maintained. Skimage (scikit-image) is similar in spirit, but has a different set of features. OpenCV is a very good C++ library with a Python interface. All of these can work with NumPy arrays and can mix and match functions from different libraries to build complex pipelines.

In the next chapter, you will learn a different form of machine learning: dimensionality reduction. As we saw in several earlier chapters, including when using images in this chapter, it is very easy to computationally generate many features. However, often we want to have a reduced number of features for speed, visualization, or to improve our results. In the next chapter, we will see how to achieve this.