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Genetic Programming for Image Classification

An Automated Approach to Feature Learning

  • Introduces a series of typical Genetic Programming-based approaches to feature learning in image classification

  • Provides broad perceptive insights on what and how Genetic Programming can offer and shows a comprehensive and systematic research route in this field

  • Presents solutions or different approaches (theoretical treatments) to solve real-world problems of image classification

  • Discusses the use of different techniques in Genetic Programming to improve the generalization performance and/or computational efficiency for image classification

Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 24)

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eBook USD 129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-65927-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 169.99
Price excludes VAT (USA)
Hardcover Book USD 169.99
Price excludes VAT (USA)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xxviii
  2. Introduction

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 1-10
  3. Computer Vision and Machine Learning

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 11-48
  4. Evolutionary Computation and Genetic Programming

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 49-74
  5. Multi-layer Representation for Binary Image Classification

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 75-95
  6. Evolutionary Deep Learning Using GP with Convolution Operators

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 97-115
  7. GP with Image Descriptors for Learning Global and Local Features

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 117-143
  8. GP with Image-Related Operators for Feature Learning

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 145-177
  9. GP for Simultaneous Feature Learning and Ensemble Learning

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 179-205
  10. Random Forest-Assisted GP for Feature Learning

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 207-226
  11. Conclusions and Future Directions

    • Ying Bi, Bing Xue, Mengjie Zhang
    Pages 227-237
  12. Back Matter

    Pages 239-258

About this book

This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.   

 


Keywords

  • Evolutionary Computation
  • Genetic Programming
  • Feature Learning
  • Image Classification
  • Computer Vision
  • Machine Learning
  • Feature Extraction
  • Feature Selection
  • Feature Construction
  • Model Interpretability

Authors and Affiliations

  • Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Ying Bi, Bing Xue, Mengjie Zhang

Bibliographic Information

  • Book Title: Genetic Programming for Image Classification

  • Book Subtitle: An Automated Approach to Feature Learning

  • Authors: Ying Bi, Bing Xue, Mengjie Zhang

  • Series Title: Adaptation, Learning, and Optimization

  • DOI: https://doi.org/10.1007/978-3-030-65927-1

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

  • Hardcover ISBN: 978-3-030-65926-4

  • Softcover ISBN: 978-3-030-65929-5

  • eBook ISBN: 978-3-030-65927-1

  • Series ISSN: 1867-4534

  • Series E-ISSN: 1867-4542

  • Edition Number: 1

  • Number of Pages: XXVIII, 258

  • Number of Illustrations: 33 b/w illustrations, 59 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

Buying options

eBook USD 129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-65927-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 169.99
Price excludes VAT (USA)
Hardcover Book USD 169.99
Price excludes VAT (USA)