In this chapter, we trained a multiclass classifier to recognize traffic signs from the GTSRB database. We discussed the basics of supervised learning, explored the intricacies of feature extraction, and extended SVMs so that they can be used for multiclass classification.

Notably, we left out some details along the way, such as attempting to fine-tune the hyperparameters of the learning algorithm. When we restrict the traffic sign dataset to only 10 classes, the default values of the various function arguments along the way, seem to be sufficient for performing exceptionally well (just look at the perfect score achieved with the HOG feature and the one-vs-one strategy). With this functional setup and a good understanding of the underlying methodology, you can now try to classify the entire GTSRB dataset! It is definitely worth taking a look at their website, where you will find classification results for a variety of classifiers. Maybe, your own approach will soon be added to the list.

In the next (and last) chapter, we will move even deeper into the field of machine learning. Specifically, we will focus on recognizing emotional expressions in human faces using convolutional neural networks. This time, we will combine the classifier with a framework for object detection, which will allow us to localize (where?) a human face in an image, and then focus on identifying (what?) the emotional expression contained in that face. This will conclude our quest into the depths of machine learning, and provide you with all the necessary tools to develop your own advanced OpenCV projects using the principles and concepts of computer vision.