Graph Convolutional Neural Networks for Computer Vision - Hardcover

Hardcover

ISBN 10: 1394356331 ISBN 13: 9781394356331

Verlag: Wiley-Scrivener, 2025

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

Malini Alagarsamy, PhD is an assistant professor at the Thiagarajar College of Engineering. She has published more than 30 research papers in journals and national and international conferences. Her research interests include software engineering, mobile application development, green computing, Internet of Things, blockchain, and machine learning.

Rajesh Kumar Dhanaraj, PhD is a Professor in the School of Computing Science and Engineering at Galgotias University. He has authored and edited more than 25 books and 53 articles in international journals and conferences and holds 21 patents. His research interests include machine learning, cyber-physical systems, and wireless sensor networks.

J. Felicia Lilian is an Assistant Professor at the Thiagarajar College of Engineering. She has published more than 10 articles in international journals and conferences. Her research interests include natural language processing, machine learning, and deep learning.

Vandana Sharma, PhD is an Associate Professor at the Amity Institute of Information Technology at the Amity University Noida Campus with more than 14 years of teaching experience. She has published 25 research papers in international journals and conferences. Her primary areas of interest include artificial intelligence, machine learning, blockchain technology, and the Internet of Things (IoT).

Gheorghita Ghinea, PhD is a Professor in the Department of Computer Science at Brunel University London. He has more than 600 publications to his credit, including book chapters and research articles in international journals of repute. His research centers on extending the notion of multimedia with that of mulsemedia, a term to denote multiple sensorial media.

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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.

Details zur Ausgabe

• Verlag: Wiley-Scrivener
• Erscheinungsdatum: 2025
• Sprache: Englisch
• ISBN 10: 1394356331
• ISBN 13: 9781394356331
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 304
• Herausgeber: Alagarsamy Malini, Dhanaraj Rajesh Kumar, Lilian J. Felicia, Sharma Vandana, Ghinea Gheorghita
• Kontakt zum Hersteller: Wiley-VCH GmbH; product_safety@wiley.com; ; Boschstrasse 12; Weinheim; 69469; Deutschland