We learned the classical feature-based approach to handling images in a machine learning context; by converting from a million pixels to a few numeric features, we were able to directly use a logistic regression classifier. All of the technologies that we learned in the other chapters suddenly became directly applicable to image problems. We saw one example in the use of image features to find similar images in a dataset.
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 enough for many irrelevant aspects of the image, such as illumination, and even 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...
