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
Introduction Pages 1-10.
Computer Vision and Machine Learning Pages
Evolutionary Computation and Genetic Programming Pages
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
GP for Simultaneous Feature Learning and Ensemble
Learning Pages 179-205.
Random Forest-Assisted GP for Feature Learning Pages
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",