02653nam 2200469 450 991064776990332120230508212501.0981-19-8570-710.1007/978-981-19-8570-6(MiAaPQ)EBC7191152(Au-PeEL)EBL7191152(CKB)26089741300041(DE-He213)978-981-19-8570-6(PPN)26820490X(EXLCZ)992608974130004120230508d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierImage co-segmentation /Avik Hati [and three others]1st ed. 2023.Singapore :Springer,[2023]©20231 online resource (231 pages)Studies in computational intelligence ;Volume 1082Print version: Hati, Avik Image Co-Segmentation Singapore : Springer,c2023 9789811985690 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.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.Studies in computational intelligence ;Volume 1082.Image segmentationImage segmentation.006.6Hati Avik1277954MiAaPQMiAaPQMiAaPQBOOK9910647769903321Image co-segmentation3364208UNINA