Graph Neural Network Methods and Applications in Scene Understanding - Brossura

Liu, Weibin; Hao, Huaqing; Wang, Hui; Zou, Zhiyuan; Xing, Weiwei

Brossura

ISBN 10: 981979935X ISBN 13: 9789819799350

Casa editrice: Springer, 2026

Riassunto

The book focuses on graph neural network methods and applications for scene understanding. Graph Neural Network is an important method for graph-structured data processing, which has strong capability of graph data learning and structural feature extraction. Scene understanding is one of the research focuses in computer vision and image processing, which realizes semantic segmentation and object recognition of image or video. In this book, the algorithm, system design and performance evaluation of scene understanding based on graph neural networks have been studied. First, the book elaborates the background and basic concepts of graph neural network and scene understanding, then introduces the operation mechanism and key methodological foundations of graph neural network. The book then comprehensively explores the implementation and architectural design of graph neural networks for scene understanding tasks, including scene parsing, human parsing, and video object segmentation. The aim of this book is to provide timely coverage of the latest advances and developments in graph neural networks and their applications to scene understanding, particularly for readers interested in research and technological innovation in machine learning, graph neural networks and computer vision. Features of the book include self-supervised feature fusion based graph convolutional network is designed for scene parsing, structure-property based graph representation learning is developed for human parsing, dynamic graph convolutional network based on multi-label learning is designed for human parsing, and graph construction and graph neural network with transformer are proposed for video object segmentation.

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Informazioni sull?autore

Weibin Liu received the Ph.D. degree in Signal and Information Processing from Institute of Information Science at Beijing Jiaotong University, China, in 2001. During 2001-2005, he was a researcher in Information Technology Division at Fujitsu Research and Development Center Co., LTD. Since 2005, he has been with the Institute of Information Science, School of Computer Science and Technology at Beijing Jiaotong University, where currently he is a professor in Digital Media Research Group. He was also a visiting researcher in Center for Human Modeling and Simulation at University of Pennsylvania, PA, USA during 2009-2010. His research interests include computer vision, video and image processing, deep learning, computer graphics, virtual human and virtual environment, and pattern recognition.

 

Huaqing Hao received the B.S. and M.S. degree in Electronic Information Engineering from Heibei University, China, in 2015 and 2018, respectively. She received the Ph.D degree in Signal and Information Processing from Institute of Information Science at Beijing Jiaotong University, China, in 2024. Currently, she is an associate professor at College of Electronic Information Engineering, Hebei University. Her main research interests include computer vision, pattern recognition and deep learning, in particular focusing on human parsing.

 

Hui Wang received the B.S. degree in Electronic Information Engineering from Hebei University, China, in 2016. He received the Ph.D degree in Signal and Information Processing from Institute of Information Science at Beijing Jiaotong University, China, in 2023. Currently, he is an associate professor at College of Electronic Information Engineering, Hebei University. His research interests include computer vision, image processing, video object segmentation.

 

Zhiyuan Zou received the B.S. degree in Software Engineering from Beijing Jiaotong University, Beijing, China, in 2015, and Ph.D. degree in Software Engineering from Institute of Information Science, Beijing Jiaotong University, in 2022. Currently, he is an associate professor at Computer School, Beijing Information Science and Technology University. His research interests include scene understanding, deep learning, computer vision, and pattern recognition.

 

Weiwei Xing received the B.S. degree in Computer Science and Technology and the Ph.D. degree in Signal and Information Processing from Beijing Jiaotong University, Beijing, China, in 2001 and 2006, respectively. She was a visiting scholar at University of Pennsylvania, PA, USA during 2011-2012. She is currently a professor at School of Software Engineering, Beijing Jiaotong University and leads the research group on Intelligent Computing and Big Data. Her research interests include computer vision, intelligent perception and applications.

 

Dalla quarta di copertina

The book focuses on graph neural network methods and applications for scene understanding. Graph Neural Network is an important method for graph-structured data processing, which has strong capability of graph data learning and structural feature extraction. Scene understanding is one of the research focuses in computer vision and image processing, which realizes semantic segmentation and object recognition of image or video. In this book, the algorithm, system design and performance evaluation of scene understanding based on graph neural networks have been studied. First, the book elaborates the background and basic concepts of graph neural network and scene understanding, then introduces the operation mechanism and key methodological foundations of graph neural network. The book then comprehensively explores the implementation and architectural design of graph neural networks for scene understanding tasks, including scene parsing, human parsing, and video object segmentation. The aim of this book is to provide timely coverage of the latest advances and developments in graph neural networks and their applications to scene understanding, particularly for readers interested in research and technological innovation in machine learning, graph neural networks and computer vision. Features of the book include self-supervised feature fusion based graph convolutional network is designed for scene parsing, structure-property based graph representation learning is developed for human parsing, dynamic graph convolutional network based on multi-label learning is designed for human parsing, and graph construction and graph neural network with transformer are proposed for video object segmentation.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 981979935X
• ISBN 13: 9789819799350
• Rilegatura: Copertina flessibile
• Numero di pagine: 236
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania