000			03753nam a22005415i 4500
001			978-981-97-2112-2
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005			20240730172406.0
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024	7		_a10.1007/978-981-97-2112-2 _2doi
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082	0	4	_a025.04 _223
100	1		_aZhang, Zheng. _eauthor. _0(orcid) _10000-0003-1470-6998 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9103488
245	1	0	_aBinary Representation Learning on Visual Images _h[electronic resource] : _bLearning to Hash for Similarity Search / _cby Zheng Zhang.
250			_a1st ed. 2024.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024.
300			_aXVI, 200 p. 45 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChapter 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			_aThis 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	_aInformation storage and retrieval systems. _922213
650		0	_aImage processing. _97417
650		0	_aArtificial intelligence _xData processing. _921787
650	1	4	_aInformation Storage and Retrieval. _923927
650	2	4	_aImage Processing. _97417
650	2	4	_aData Science. _934092
710	2		_aSpringerLink (Online service) _9103493
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789819721115
776	0	8	_iPrinted edition: _z9789819721139
776	0	8	_iPrinted edition: _z9789819721146
856	4	0	_uhttps://doi.org/10.1007/978-981-97-2112-2
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