Image co-segmentation / Avik Hati, Rajbabu Velmurugan, Sayan Banerjee, Subhasis Chaudhuri.
2023
TA1638.4
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Details
Title
Image co-segmentation / Avik Hati, Rajbabu Velmurugan, Sayan Banerjee, Subhasis Chaudhuri.
Author
Hati, Avik, author.
ISBN
9789811985706 (electronic bk.)
9811985707 (electronic bk.)
9789811985690
9811985707 (electronic bk.)
9789811985690
Published
Singapore : Springer, [2023]
Copyright
©2023
Language
English
Description
1 online resource (xiv, 221 pages) : illustrations (chiefly color),
Item Number
10.1007/978-981-19-8570-6 doi
Call Number
TA1638.4
Dewey Decimal Classification
006.6
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 6, 2023).
Series
Studies in computational intelligence ; volume 1082. 1860-9503
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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.
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.