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Binary representation learning on visual imageslearning to hash for similarity search /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Binary representation learning on visual imagesby Zheng Zhang.
Reminder of title:	learning to hash for similarity search /
Author:	Zhang, Zheng.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xvi, 200 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Hashing (Computer science)
Online resource:	https://doi.org/10.1007/978-981-97-2112-2
ISBN:	9789819721122$q(electronic bk.)

Binary representation learning on visual imageslearning to hash for similarity search /
Zhang, Zheng.

Binary representation learning on visual imageslearning to hash for similarity search /[electronic resource] :by Zheng Zhang. - Singapore :Springer Nature Singapore :2024. - xvi, 200 p. :ill., digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Scalable Supervised Asymmetric Hashing -- Chapter 3. Inductive Structure Consistent Hashing -- Chapter 4. Probability Ordinal-preserving Semantic Hashing -- Chapter 5. Ordinal-preserving Latent Graph Hashing -- Chapter 6. Deep Collaborative Graph Hashing -- Chapter 7. Semantic-Aware Adversarial Training -- Index.

This book introduces pioneering developments in binary representation learning on visual images, a state-of-the-art data transformation methodology within the fields of machine learning and multimedia. Binary representation learning, often known as learning to hash or hashing, excels in converting high-dimensional data into compact binary codes meanwhile preserving the semantic attributes and maintaining the similarity measurements. The book provides a comprehensive introduction to the latest research in hashing-based visual image retrieval, with a focus on binary representations. These representations are crucial in enabling fast and reliable feature extraction and similarity assessments on large-scale data. This book offers an insightful analysis of various research methodologies in binary representation learning for visual images, ranging from basis shallow hashing, advanced high-order similarity-preserving hashing, deep hashing, as well as adversarial and robust deep hashing techniques. These approaches can empower readers to proficiently grasp the fundamental principles of the traditional and state-of-the-art methods in binary representations, modeling, and learning. The theories and methodologies of binary representation learning expounded in this book will be beneficial to readers from diverse domains such as machine learning, multimedia, social network analysis, web search, information retrieval, data mining, and others.

ISBN: 9789819721122$q(electronic bk.)

Standard No.: 10.1007/978-981-97-2112-2doiSubjects--Topical Terms:

611851
Hashing (Computer science)

LC Class. No.: QA76.9.H36

Dewey Class. No.: 005.741

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505 0 $a Chapter 1. Introduction -- Chapter 2. Scalable Supervised Asymmetric Hashing -- Chapter 3. Inductive Structure Consistent Hashing -- Chapter 4. Probability Ordinal-preserving Semantic Hashing -- Chapter 5. Ordinal-preserving Latent Graph Hashing -- Chapter 6. Deep Collaborative Graph Hashing -- Chapter 7. Semantic-Aware Adversarial Training -- Index.
520 $a This book introduces pioneering developments in binary representation learning on visual images, a state-of-the-art data transformation methodology within the fields of machine learning and multimedia. Binary representation learning, often known as learning to hash or hashing, excels in converting high-dimensional data into compact binary codes meanwhile preserving the semantic attributes and maintaining the similarity measurements. The book provides a comprehensive introduction to the latest research in hashing-based visual image retrieval, with a focus on binary representations. These representations are crucial in enabling fast and reliable feature extraction and similarity assessments on large-scale data. This book offers an insightful analysis of various research methodologies in binary representation learning for visual images, ranging from basis shallow hashing, advanced high-order similarity-preserving hashing, deep hashing, as well as adversarial and robust deep hashing techniques. These approaches can empower readers to proficiently grasp the fundamental principles of the traditional and state-of-the-art methods in binary representations, modeling, and learning. The theories and methodologies of binary representation learning expounded in this book will be beneficial to readers from diverse domains such as machine learning, multimedia, social network analysis, web search, information retrieval, data mining, and others.
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