Structural Pattern Recognition using Graph Matching
Approximate and Error-Tolerant Algorithms
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 29 September 2025
- ISBN 9781032850344
- Binding Hardback
- No. of pages250 pages
- Size 234x156 mm
- Weight 620 g
- Language English
- Illustrations 54 Illustrations, black & white; 1 Halftones, black & white; 53 Line drawings, black & white; 20 Tables, black & white 699
Categories
Short description:
This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation.
More
Long description:
This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science.
• Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations
• Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching
• Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach
• Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems
• Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs)
MoreTable of Contents:
1. Introduction 2. Structural Pattern Recognition 3. Graph Matching Algorithms: A Survey 4. Graph Matching using Extensions to Graph Edit Distance 5. Graph Matching using Centrality Measures 6. Geometric Graph Matching 7. Graph Kernels and Embedding 8. Graph Matching in Image Analysis 9. Graph Matching in Social Network Analysis 10. Recent Advances and Future Directions. A. Graph Matching Tools
Bibliography
Index
