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Image co-segmentation

Hati, Avik.

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Image co-segmentation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Image co-segmentation/ by Avik Hati ... [et al.].
other author:	Hati, Avik.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xiv, 221 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Image segmentation. -
Online resource:	https://doi.org/10.1007/978-981-19-8570-6
ISBN:	9789811985706

Image co-segmentation
Image co-segmentation[electronic resource] /by Avik Hati ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xiv, 221 p. :ill. (chiefly color), digital ;24 cm. - Studies in computational intelligence,v. 10821860-9503 ;. - Studies in computational intelligence ;v. 1082..

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.

ISBN: 9789811985706

Standard No.: 10.1007/978-981-19-8570-6doiSubjects--Topical Terms:

2132796
Image segmentation.

LC Class. No.: TA1638.4

Dewey Class. No.: 006.6

Image co-segmentation
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245 0 0 $a Image co-segmentation $h [electronic resource] / $c by Avik Hati ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xiv, 221 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1082
505 0 $a 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.
520 $a 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.
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650 1 4 $a Signal, Speech and Image Processing. $3 3592727
650 2 4 $a Digital and Analog Signal Processing. $3 3538815
650 2 4 $a Image Processing. $3 891209
700 1 $a Hati, Avik. $3 3628769
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830 0 $a Studies in computational intelligence ; $v v. 1082. $3 3628770
856 4 0 $u https://doi.org/10.1007/978-981-19-8570-6
950 $a Engineering (SpringerNature-11647)