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This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.
Introduction
Survey of Image Co-segmentation
Mathematical Background
Maximum Common Subgraph Matching
Maximally Occurring Common Subgraph Matching
Co-segmentation Using a Classification Framework
Co-segmentation Using Graph Convolutional Network
Conditional Siamese Convolutional Network
Few-shot Learning for Co-segmentation
Conclusions