Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Table of Contents (13 chapters)

Summary

In this chapter, we talked about a typical workflow to deal with machine learning problems: how we can extract informative features from raw data, how we can use data and labels to train a machine learning model, and how we can use the finalized model to predict new data labels. We learned that it is essential to split data into a training set and test set, as this is the only way to know how well a model will generalize to new data points.

On the software side of things, we significantly improved our Python skills. We learned how to use NumPy arrays to store and manipulate data and how to use Matplotlib for data visualization. We talked about scikit-learn and its many useful data resources. Finally, we also addressed OpenCV's own TrainData container, which provides some relief for users of OpenCV's C++ API.

With these tools in hand, we are now ready to implement...