Book Image

Machine Learning for OpenCV 4 - Second Edition

By : Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler

Book Image

Machine Learning for OpenCV 4 - Second Edition

By: Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler

Overview of this book

OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition. You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system. By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: Fundamentals of Machine Learning and OpenCV

Section 1: Fundamentals of Machine Learning and OpenCV

A Taste of Machine Learning

A Taste of Machine Learning

Technical requirements

Getting started with machine learning

Problems that machine learning can solve

Getting started with Python

Getting started with OpenCV

Applications of machine learning

What's new in OpenCV 4.0?

Working with Data in OpenCV

Working with Data in OpenCV

Technical requirements

Understanding the machine learning workflow

Dealing with data using OpenCV and Python

First Steps in Supervised Learning

First Steps in Supervised Learning

Technical requirements

Understanding supervised learning

Using classification models to predict class labels

Using regression models to predict continuous outcomes

Classifying iris species using logistic regression

Representing Data and Engineering Features

Representing Data and Engineering Features

Technical requirements

Understanding feature engineering

Preprocessing data

Understanding dimensionality reduction

Representing categorical variables

Representing text features

Representing images

Section 2: Operations with OpenCV

Section 2: Operations with OpenCV

Using Decision Trees to Make a Medical Diagnosis

Using Decision Trees to Make a Medical Diagnosis

Technical requirements

Understanding decision trees

Using decision trees to diagnose breast cancer

Using decision trees for regression

Detecting Pedestrians with Support Vector Machines

Detecting Pedestrians with Support Vector Machines

Technical requirement

Understanding linear SVMs

Dealing with nonlinear decision boundaries

Detecting pedestrians in the wild

Implementing a Spam Filter with Bayesian Learning

Implementing a Spam Filter with Bayesian Learning

Technical requirements

Understanding Bayesian inference

Implementing your first Bayesian classifier

Classifying emails using the Naive Bayes classifier

Discovering Hidden Structures with Unsupervised Learning

Discovering Hidden Structures with Unsupervised Learning

Technical requirements

Understanding unsupervised learning

Understanding k-means clustering

Understanding expectation-maximization

Compressing color spaces using k-means

Classifying handwritten digits using k-means

Organizing clusters as a hierarchical tree

Section 3: Advanced Machine Learning with OpenCV

Section 3: Advanced Machine Learning with OpenCV

Using Deep Learning to Classify Handwritten Digits

Using Deep Learning to Classify Handwritten Digits

Technical requirements

Understanding the McCulloch-Pitts neuron

Understanding the perceptron

Implementing your first perceptron

Understanding multilayer perceptrons

Getting acquainted with deep learning

Classifying handwritten digits

Ensemble Methods for Classification

Ensemble Methods for Classification

Technical requirements

Understanding ensemble methods

Combining decision trees into a random forest

Using random forests for face recognition

Implementing AdaBoost

Combining different models into a voting classifier

Selecting the Right Model with Hyperparameter Tuning

Selecting the Right Model with Hyperparameter Tuning

Technical requirements

Evaluating a model

Understanding cross-validation

Estimating robustness using bootstrapping

Assessing the significance of our results

Tuning hyperparameters with grid search

Scoring models using different evaluation metrics

Chaining algorithms together to form a pipeline

Using OpenVINO with OpenCV

Using OpenVINO with OpenCV

Technical requirements

Introduction to OpenVINO

OpenVINO toolkit installation

Interactive face detection demo

Using OpenVINO Inference Engine with OpenCV

Using OpenVINO Model Zoo with OpenCV

Image classification using OpenCV with OpenVINO Inference Engine

Conclusion

Technical requirements

Approaching a machine learning problem

Building your own estimator

Where to go from here

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Classifying emails using the Naive Bayes classifier

The final task of this chapter will be to apply our newly gained skills to a real spam filter! This task deals with solving a binary-class (spam/ham) classification problem using the Naive Bayes algorithm.

Naive Bayes classifiers are actually a very popular model for email filtering. Their naivety lends itself nicely to the analysis of text data, where each feature is a word (or a bag of words), and it would not be feasible to model the dependence of every word on every other word.

There are a bunch of good email datasets out there, such as the following:

The Hewlett-Packard spam database: https://archive.ics.uci.edu/ml/machine-learning-databases/spambase
The Enrom-Spam dataset: http://www.aueb.gr/users/ion/data/enron-spam

In this section, we will be using the Enrom-Spam dataset, which can be downloaded for free from the given...