Description
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.
Table of Contents
Introduction.- Survey of Image Co-segmentation.- Mathematical Background.- Co-segmentation using a Classification Framework.- Use of Maximum Common Subgraph Matching.- Maximally Occurring Common Subgraph Matching.- Co-segmentation using Graph Convolutional Neural Network.- Use of a Conditional Siamese Convolutional Network.- Few-shot Learning for Co-segmentation.- Conclusions.
