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 went deep down the rabbit hole and looked at a number of common feature engineering techniques, focusing on both feature selection and feature extraction. We successfully formatted, cleaned, and transformed data so that it could be understood by common machine learning algorithms. We learned about the curse of dimensionality and dabbled a bit in dimensionality reduction by implementing PCA in OpenCV. Finally, we took a short tour of common feature extraction techniques that OpenCV provides for image data.

With these skills under our belt, we are now ready to take on any data, be it numerical, categorical, text, or image data. We know exactly what to do when we encounter missing data, and we know how to transfer our data to make it fit our preferred machine learning algorithm.

In the next chapter, we will take the next step and talk about a specific...