Hati Avik, Velmurugan R., Banerjee S., Chaudhuri S. Image Co-segmentation

Springer, 2023. — 231 p. — (Studies in Computational Intelligence 1082). — ISBN: 978-981-19-8569-0.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.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.