Binary Representation Learning on Visual Images (Record no. 88273)

000 -LEADER
fixed length control field 03753nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-981-97-2112-2
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730172406.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240608s2024 si | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789819721122
-- 978-981-97-2112-2
082 04 - CLASSIFICATION NUMBER
Call Number 025.04
100 1# - AUTHOR NAME
Author Zhang, Zheng.
245 10 - TITLE STATEMENT
Title Binary Representation Learning on Visual Images
Sub Title Learning to Hash for Similarity Search /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVI, 200 p. 45 illus. in color.
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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 ## - SUMMARY, ETC.
Summary, etc 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.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
General subdivision Data processing.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-981-97-2112-2
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
100 1# - AUTHOR NAME
-- (orcid)
-- 0000-0003-1470-6998
264 #1 -
-- Singapore :
-- Springer Nature Singapore :
-- Imprint: Springer,
-- 2024.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information storage and retrieval systems.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image processing.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information Storage and Retrieval.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image Processing.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Science.
912 ## -
-- ZDB-2-SCS
912 ## -
-- ZDB-2-SXCS

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