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Image co-segmentation

Hati, Avik.

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Image co-segmentation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Image co-segmentation/ by Avik Hati ... [et al.].
其他作者:	Hati, Avik.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xiv, 221 p. :ill. (chiefly color), digital ;24 cm.
內容註:	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
標題:	Image segmentation. -
電子資源:	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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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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