Overview
- 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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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
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 andpostgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.
Authors and Affiliations
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: 978-3-030-65926-4Published: 09 February 2021
Softcover ISBN: 978-3-030-65929-5Published: 10 February 2022
eBook ISBN: 978-3-030-65927-1Published: 08 February 2021
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