Ying Bi, Bing Xue, Mengjie Zhang

Genetic Programming for Image Classification

An Automated Approach to Feature Learning

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Adaptation, Learning, and Optimization 24
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-65929-5
• 1st ed. 2021
• Seitenzahl: 288
• Erscheinungstermin: 10. Februar 2022
• Englisch
• Abmessung: 235mm x 155mm x 16mm
• Gewicht: 441g
• ISBN-13: 9783030659295
• ISBN-10: 3030659291
• Artikelnr.: 63292266

Inhaltsangabe

Computer Vision and Machine Learning.- Evolutionary Computation and Genetic Programming.- Multi-Layer Representation for Binary Image Classification.- Evolutionary Deep Learning Using GP with Convolution Operators.- GP with Image Descriptors for Learning Global and Local Features.- GP with Image-Related Operators for Feature Learning.- GP for Simultaneous Feature Learning and Ensemble Learning.- Random Forest-Assisted GP for Feature Learning.- Conclusions and Future Directions.