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:
Preface xv
1 Role of Graph Convolutional Neural Networks (GCNN) in Computer Vision Applications 1
A. Malini, Vandana Sharma, J. Felicia Lilian, Rajesh Kumar Dhanaraj, Sharangapriyan S. and Shrinivas S.
1.1 Introduction 2
1.2 Understanding Convolutional Neural Network in Computer Vision 2
1.3 Core Components of CNN 3
1.4 Extending CNNs to Handle Graph-Structured Data 3
1.5 Application of GCNN in Computer Vision 6
1.6 Enhancing Performance and Interpretability with GCNN 8
1.7 Future Directions and Emerging Trends 10
1.8 Challenges and Open Research Questions 13
1.9 Case Studies: Real-World Applications 16
1.10 Conclusion 18
2 Scene Graph Generation from Static Images: Overview, Methods, and Applications 21
K. Krishnakishore, R. Vijayarangan, V. Jagan Naveen and V. Kannan
2.1 Introduction 22
2.2 Definition 24
2.3 Challenge 25
2.4 Scene Graph Generation 25
2.5 Static Image 25
2.6 Degradation of a Static Image 26
2.7 Method 1: Wavelet Feature Extraction 29
2.8 Psychological Perspective 32
2.9 Linguistic Perspective 33
2.10 Concepts and Conceptual Structures in Artificial Intelligence Perspective 35
2.11 Applications of CGS 37
2.12 Linguistic and Psychological Perspective 39
2.13 Image Synthesis from Layouts 41
2.14 Method Comparison 42
2.15 Conclusion 43
3 Transformation from CNN to Graph-Structured Data: Node Classification and Edge Prediction 47
R. Vijayarangan, R. Satish Kumar, K. Umadevi and K. Ashok Kumar
3.1 Why Graphs 48
3.2 SVM (Support Vector Machine) 57
3.3 XGBOOST 58
3.4 Artificial Neural Network (ANN) 59
3.5 Auto Encoder (AE) 62
3.6 Demographic and Related Data: Health Condition, Type of Gender, Age, Family Condition 63
3.7 Naïve Bayes (NB) 64
3.8 Random Forest (RF) 66
3.9 Conclusions 68
4 Research Trends and Challenges of GCNN Over CNN and Digital Image Processing Techniques 73
Rithish Kanna S., Suganthi P. and Kavitha P.
4.1 Introduction 74
4.2 Introduction to Convolutional Neural Network 75
4.3 Neural Style Transfer—Artistic View 78
4.4 Various Existing Works of NST 79
4.5 Hybrid Neural Style Transfer 81
4.6 Implementation of HNST 85
4.7 Results and Inference 86
4.8 Further Ideas of HNST 91
4.9 Conclusion 92
5 Classification of Graph Filtering Operations and Inductive Learning by Exploiting Multiple Graphs in GCNN 95
S. Kayalvizhi, Harish Sekar and Prasanna Guptha M.P.
5.1 Introduction 96
5.2 Graph Basics 96
5.3 Graph Convolutional Filters 98
5.4 Graph Filter Banks 107
5.5 Graph Neural Networks 110
5.6 Conclusion 112
6 GCNN with Adaptive Filters for Hyperspectral Image Classification 117
U. Moulali, R. Vijayarangan, S. Khaleel Ahamed and Kamakshaiah Kolli
6.1 Introduction 118
6.2 Related Works 120
6.3 Classification of Graph Filtering Operations 123
6.4 Experimental Analysis and Discussion 134
6.5 Conclusion 136
7 Graph Convolution Neural Network on Human Motion Prediction 141
B. Subbulakshmi, M. Nirmala Devi and Srimadhi J.
7.1 Introduction 141
7.2 Graph Convolution Neural Network (GCN) 146
7.3 Forms of GCN on Human Motion Prediction 148
7.4 Types of Graphs Employed on GCN 156
7.5 Conclusion 157
8 GraphChXNet: A Graph Convolutional Neural Network-Based Model for Detecting Chest Diseases Using X-Ray Images 161
D. Kiruthika, N. Vinothini, G. Jegan and G. Ananthi
8.1 Introduction 162
8.2 Proposed Methodology 164
8.3 Results and Discussion 171
8.4 Conclusion 178
9 Aspect-Based Sentiment Analysis Using GCN 181
Sachin K., Santhosh K.M.R., Sugindar A.D. and J. Felicia Lilian
9.1 Introduction 181
9.2 GCN and ABSA 185
9.3 Advancements of GCN and ABSA over the Years 189
9.4 Advancement of Technology with GCN and Algorithm Used 196
9.5 Case Study on GCN Application: Recommendation Systems 199
9.6 Summary 202
10 Analysis and Classification Using Graph Convolutional Neural Networks in Medical Imaging 205
M. Suguna and Priya Thiagarajan
10.1 Introduction 206
10.2 Literature Review—GCNN in Healthcare 210
10.3 Methodology 213
10.4 Results and Discussion 218
10.5 Conclusion 220
11 Case Studies and Real-World Applications of Graph Convolutional Networks in Computer Vision 225
Yogeesh N.
11.1 Introduction 226
11.2 Graph Convolutional Networks: A Brief Review 228
11.3 Case Study 1: Graph Convolutional Networks for Image Classification 231
11.4 Case Study 2: Object Detection and Localization Using Graph Convolutional Networks 236
11.5 Case Study 3: Semantic Segmentation with Graph Convolutional Networks 238
11.6 Case Study 4: 3D Vision and Point Cloud Processing of Graph Convolutional Networks 240
11.7 Case Study 5: Graph Convolutional Networks for Video Understanding and Action Recognition 243
11.8 Other Notable Case Studies and Applications 244
11.9 Discussion and Future Directions 249
11.10 Conclusion 250
12 Case Study and Use Cases of Dynamic Graphs in GCNN for Computer Vision 255
S. Anubha Pearline and S. Geetha
12.1 Introduction 255
12.2 Graph Convolutional Neural Networks (GCNNs) 259
12.3 GCNN Case Studies 265
12.4 Challenges and Issues in GCNN for CV 270
12.5 Conclusion 270
References 271
About the Editors 275
Index 279
