This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation.
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)
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
Dr. Shri Prakash Dwivedi is an esteemed researcher and academic in the field of Computer Science and Engineering, specializing in pattern recognition, graph matching, and algorithms. He is currently serving as an Assistant Professor in the Department of Information Technology at G.B. Pant University of Agriculture & Technology, Pantnagar, where he has been actively involved in teaching and research. Dr. Dwivedi obtained his Ph.D. in Computer Science and Engineering from the Indian Institute of Technology (BHU), Varanasi, where he worked on graph matching algorithms for structural pattern recognition. Prior to that, he earned his M.E. in Computer Science from the Indian Institute of Science (IISc), Bangalore. Dr. Dwivedi has published extensively in leading journals and conferences, including Pattern Recognition Letters, Springers Lecture Notes in Computer Science, Lecture Notes in Electrical Engineering, ACM and IEEE. His work primarily focuses on error-tolerant graph matching, similarity measure, approximate algorithms, and computational methods in structural pattern recognition.
Beyond academia, Dr. Dwivedi has industry experience as a Software Engineer (R&D) at various software companies, where he worked on scientific computing, algorithm design, and software development. He has also contributed to research projects integrating IoT and cloud-based automation systems. A recipient of several accolades, Dr. Dwivedi secured All India Rank 1 in GATE 2006 and has been a holder of the MHRD Scholarship for his academic excellence.
Dr. Ravi Shankar Singh is working as an Associate Professor in the Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, India and has an academic background of B.Tech.(CSE), M.Tech.(CSE) and Ph.D.(CSE). He has been a Senior Member of IEEE and ACM. More than 50 research papers have been included in his account. His research interests include high-performance and cloud computing.
