An Introduction to Image Classification, Toennies K.D., 2024

An Introduction to Image Classification, Toennies K.D., 2024.
    
   This book commences by laving the groundwork for image classification through image feature extraction and model-driven classification in feature space. Neural networks are then introduced that can learn arbitrary models directly from labeled sample images. The behavior of a neural network is traced back to concepts of a designed classification model. We explicitly discuss relations between network topologies and their ability to represent components of a traditional designed model. It helps to gain insight into the potential and limitations of such networks.




Python Implementation Details.
Many of the methods that we will discuss in the book can be explored experimentally. We will use Python to demonstrate image classification methods. Each chapter will conclude with one or two small programming projects in Python where you can carry out these experiments by yourself.

If you have no experience in Python programming, we suggest that you complete one of the many free courses on Python from the internet. You should be familiar with notation, basic data structures, and control structures before jumping into the exercises. Necessary modules, objects, and methods in Python that are specific to image classification tasks will be explained in the text. It is intended to let you gradually develop programming skills to solve image classification tasks.

Contents.
Cover.
Front Matter.
1. Image Classification: A Computer Vision Task.
2. Image Features: Extraction and Categories.
3. Feature Reduction.
4. Bayesian Image Classification in Feature Space.
5. Distance-Based Classifiers.
6. Decision Boundaries in Feature Space.
7. 7 Multi-Layer Perceptron for Image Classification.
8. Feature Extraction by Convolutional Neural Network.
9. Network Set-Up for Image Classification.
10. Basic Network Training for Image Classification.
11. Dealing with Training Deficiencies.
12. Learning Effects and Network Decisions Back Matter.



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