- author = "Ying Bi and Bing Xue and Mengjie Zhang",
- title = "Genetic Programming for Image Classification",
- publisher = "Springer Nature",
- year = "2021",
- volume = "24",
- series = "Adaptation, Learning, and Optimization book series",
- month = feb # " 9",
- note = "An Automated Approach to Feature Learning",
- keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Feature Learning, Image Classification, Computer Vision, Machine Learning, Feature Extraction, Feature Selection, Feature Construction, Model Interpretability",
- isbn13 = "978-3030659264",
- ISSN = "1867-4534",
-
URL = "
https://link.springer.com/book/10.1007/978-3-030-65927-1",
-
URL = "https://www.amazon.com/Genetic-Programming-Image-Classification-Optimization-dp-3030659267/dp/3030659267",
-
code_url = "https://github.com/YingBi92/BookCode",
-
DOI = "
doi:10.1007/978-3-030-65927-1",
- size = "286 pages",
-
abstract = "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. Front Matter Pages
i-xxviii.
Introduction Pages 1-10.
Computer Vision and Machine Learning Pages 11-48.
Evolutionary Computation and Genetic Programming Pages 49-74.
Multi-layer Representation for Binary Image Classification Pages 75-95.
Evolutionary Deep Learning Using GP with Convolution Operators Pages 97-115.
GP with Image Descriptors for Learning Global and Local Features Pages 117-143.
GP with Image-Related Operators for Feature Learning Pages 145-177.
GP for Simultaneous Feature Learning and Ensemble Learning Pages 179-205.
Random Forest-Assisted GP for Feature Learning Pages 207-226.
Conclusions and Future Directions Pages 227-237.
Back Matter Pages 239-258.
Evolutionary Computation Research Group, School of Engineering and Computer Science Victoria University of Wellington, Wellington, New Zealand",
- notes = "Reviewed in \cite{Zafra:2022:GPEM}",
