Название: Graph Convolutional Neural Networks for Computer Vision
Автор: Malini Alagarsamy, Rajesh Kumar Dhanaraj, J. Felicia Lilian, Vandana Sharma, Gheorghita Ghinea
Издательство: Wiley-Scrivener
Год: 2026
Страниц: 297
Язык: английский
Формат: pdf, epub (true)
Размер: 40.6 MB

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 (NLP). 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.

Graph convolutional network (GCN) models are neural network architectures that can use the graph structure to aggregate node information from neighbors in a convolutional manner. Graph convolutional networks have obtained higher performance in a wide range of tasks and applications because of their high interpretive capability in learning graph representations. Many real-world problems involve more complex data structures where elements are related in non-grid ways. Graph Convolutional Neural Networks (GCNNs) are powerful tools designed to handle such graph-structured data. In contrast, graph convolutional networks have been used to address several computer vision issues with equivalent or even superior results. They capture relationships and dependencies between data points, making them useful for a wide range of visual tasks. This book introduces the strengths of GCNNs in solving various computer vision problems. It combines both theory and practical applications to give readers a complete picture. The first chapter explains the role of GCNNs in vision systems and how they enhance image understanding. One important application is scene graph generation, where GCNNs model the relationships between objects in a static image. Another chapter focuses on how CNN results can be transformed into graph data for node classification and edge prediction.

The hybrid neural style transfer is implemented using Python programming. There are various deep-learning libraries available in Python that deal with the images. So this process of HNST can be implemented using the Python libraries that deal with image processing and deep learning algorithms. The usage of some of the important Python Libraries on the images is shown below. An OpenCV is a collection of Python bindings. Python was created to address various issues regarding computer vision. A window containing an image is displayed using the cv2.imshow() technique. The image size is automatically adjusted for the window. OpenCV makes it possible to carry out various tasks of image processing and computer vision.

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