Image Classification: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning

Image Classification

Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning

By Dr. Mark Magic

Long Island, NY, United States

The author and the editor have taken care in the preparation of this book and taken great efforts to ensure that the information and instructions contained in this book are accurate, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions.

No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein. Use of the contents contained in this book is at your own risk.

If any code samples or techniques contained or described in this book is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

Image Classification: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning

Copyright 2019 Dr. Mark Magic All rights reserved.

Published by M.J. Magic Publishing. This publication is protected by copyright, and permission must be obtained from the author prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission to use material from this work, please submit a written request to Dr. Mark Magic: [email protected].

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First edition: June 2019

Table of Contents

Chapter 1: Introduction

Chapter 2: Tiny Images Representation and Classifiers

Chapter 3: HOG Features Representation and Classifiers

Chapter 4: Bag of SIFT Features Representation and Classifiers

Chapter 5: Training a Convolutional Neural Network from Scratch

Chapter 6: Fine Tuning a Pre-Trained Deep Network (AlexNet)

Chapter 7: Pre-Trained Deep Network (AlexNet) Features Representation and Classifiers

Chapter 8: GPU Compatible and Google Colaboratory

Chapter 9: Conclusions

References

Postscript

About Dr. Mark Magic

Connect with Dr. Mark Magic

Other books by Dr. Mark Magic

Chapter 1: Introduction

Image classification is a hot topic and one of the core problems in the fields of Computer Vision and Machine Learning. It refers to the task of assigning a label from a fixed set of categories to an input image. Many other seemingly distinct tasks in the fields, such as object detection and segmentation, can be reduced to image classification.

Depending on the interaction between the analyst and the computer during classification, there are two types of image classification: supervised and unsupervised.

For supervised classification, example images with their correct labels are provided as the training dataset. A learning algorithm analyzes the training dataset and then produces an inferred function, which can be used for classifying new images.

For unsupervised classification, the training data are not labeled, that is to say, without the analyst’s intervention. An algorithm learns from the data, identifies commonalities in the data, and reacts based on the presence or absence of such commonalities in each new piece of data.

In this book, all the algorithms are supervised.

Python will be used in this book to realize the classification algorithms. Python [¹] is an interpreted, high-level, general-purpose programming language. Python was created by Guido van Rossum and first released in 1991. It has a design philosophy of emphasizing code readability, notably using significant whitespace. It features a dynamic type system and automatic memory management. It supports multiple programming paradigms, including object-oriented, imperative, functional and procedural. It has a large and comprehensive standard library.

The Anaconda Distributions of Python can be downloaded from https://www.anaconda.com/download. Both the 2.7 and 3.7 versions need to be used in this book.

For Windows 64-bit Operating Systems, after downloading Anaconda2-2018.12-Windows-x86_64.exe and Anaconda3-2018.12-Windows-x86_64.exe, first install them with default settings; then open Anaconda Prompt to install several libraries that will be used later. For Python 2.7 version, we need to install opencv-contrib-python using pip install opencv-contrib-python==3.3.0.9. For Python 3.7 version, we need to install opencv-python using pip install opencv-python==3.4.3.18, install PyTorch using "pip install https://download.pytorch.org/whl/cpu/torch-1.0.0-cp37-cp37m-win_amd64.whl", and install torchvision using