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)

Classifying handwritten digits

In the previous section, we covered a lot of the theory around neural networks, which can be a little overwhelming if you are new to this topic. In this section, we will classify handwritten digits from the popular MNIST dataset, which has been constructed by Yann LeCun and colleagues and serves as a popular benchmark dataset for machine learning algorithms.

We will train two different networks on it:

  • a multilayer perceptron using OpenCV
  • a deep neural net using Keras

Loading the MNIST dataset

The easiest way to obtain the MNIST dataset is using Keras:

In [1]: from keras.datasets import mnist
... (X_train, y_train), (X_test, y_test) = mnist.load_data()
Out[1]: Using TensorFlow backend.