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.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Fundamentals of Machine Learning and OpenCV
Section 1: Fundamentals of Machine Learning and OpenCV
2
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
Installation
Applications of machine learning
What's new in OpenCV 4.0?
Summary
3
Working with Data in OpenCV
Working with Data in OpenCV
Technical requirements
Understanding the machine learning workflow
Dealing with data using OpenCV and Python
Summary
4
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
Summary
5
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
Summary
6
Section 2: Operations with OpenCV
Section 2: Operations with OpenCV
7
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
Summary
8
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
Summary
9
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
Summary
10
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
Summary
11
Section 3: Advanced Machine Learning with OpenCV
Section 3: Advanced Machine Learning with OpenCV
12
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
Summary
13
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
Summary
14
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
Summary
15
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
Summary
16
Conclusion
Conclusion
Technical requirements
Approaching a machine learning problem
Building your own estimator
Where to go from here
Summary
17
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Understanding expectation-maximization

k-means clustering is but one concrete application of a more general algorithm known as expectation-maximization. In short, the algorithm works as follows:

  1. Start with some random cluster centers.
  2. Repeat until convergence:
    • Expectation step: Assign all data points to their nearest cluster center.
    • Maximization step: Update the cluster centers by taking the mean of all of the points in the cluster.

Here, the expectation step is so named because it involves updating our expectation of which cluster each point in the dataset belongs to. The maximization step is so named because it involves maximizing a fitness function that defines the location of the cluster centers. In the case of k-means, maximization is performed by taking the arithmetic mean of all of the data points in a cluster.

This should become clearer with the following screenshot...

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