Graph Convolutional Neural Networks for Computer Vision

Malini Alagarsamy, Rajesh Kumar Dhanaraj, J. Felicia Lilian, Vandana Sharma, Gheorghita Ghinea

• Publisher: Wiley-Scrivener
• Year: 2025
• Print ISBN: 9781394356331
• eBook ISBN: 9781394356348
• Edition: 1
• Topic: Mathematics
• Subtopic: Mathematics General
• Index: Mathematics

Revolutionize your machine learning practice with this essential book that provides expert insights into leveraging Graph Convolutional Networks (GCNNs) to overcome the limitations of traditional CNNs.

In the last decade, computer vision has become a major focus for addressing the world's growing processing needs. Many existing deep learning architectures for computer vision challenges are based on convolutional neural networks (CNNs). Despite their great achievements, CNNs struggle to encode the intrinsic graph patterns in specific learning tasks. In contrast, graph convolutional networks have been used to address several computer vision issues with equivalent or superior results. The use of GCNNs has shown significant achievement in image classifications, video understanding, point clouds, meshes, and other applications in natural language processing. This book focuses on the applications of graph convolutional networks in computer vision. Through expert insights, it explores how researchers are finding ways to perform convolution algorithms on graphs to improve the way we use machine learning.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Preface
• 1 Role of Graph Convolutional Neural Networks (GCNN) in Computer Vision Applications
• 2 Scene Graph Generation from Static Images: Overview, Methods, and Applications
• 3 Transformation from CNN to Graph-Structured Data: Node Classification and Edge Prediction
• 4 Research Trends and Challenges of GCNN Over CNN and Digital Image Processing Techniques
• 5 Classification of Graph Filtering Operations and Inductive Learning by Exploiting Multiple Graphs in GCNN
• 6 GCNN with Adaptive Filters for Hyperspectral Image Classification
• 7 Graph Convolution Neural Network on Human Motion Prediction
• 8 GraphChXNet: A Graph Convolutional Neural Network-Based Model for Detecting Chest Diseases Using X-Ray Images
• 9 Aspect-Based Sentiment Analysis Using GCN
• 10 Analysis and Classification Using Graph Convolutional Neural Networks in Medical Imaging
• 11 Case Studies and Real-World Applications of Graph Convolutional Networks in Computer Vision
• 12 Case Study and Use Cases of Dynamic Graphs in GCNN for Computer Vision
• About the Editors
• Index
• Also of Interest
• End User License Agreement