LEADER 04111nam 22006615 450 001 9910865233003321 005 20251215121325.0 010 $a9789819721122$b(electronic bk.) 010 $z9789819721115 024 7 $a10.1007/978-981-97-2112-2 035 $a(MiAaPQ)EBC31462178 035 $a(Au-PeEL)EBL31462178 035 $a(CKB)32271062200041 035 $a(DE-He213)978-981-97-2112-2 035 $a(MiAaPQ)EBC31574358 035 $a(Au-PeEL)EBL31574358 035 $a(EXLCZ)9932271062200041 100 $a20240608d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBinary Representation Learning on Visual Images $eLearning to Hash for Similarity Search /$fby Zheng Zhang 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (210 pages) 311 08$aPrint version: Zhang, Zheng Binary Representation Learning on Visual Images Singapore : Springer Singapore Pte. Limited,c2024 9789819721115 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aInformation storage and retrieval systems 606 $aImage processing 606 $aArtificial intelligence$xData processing 606 $aInformation Storage and Retrieval 606 $aImage Processing 606 $aData Science 606 $aSistemes d'informació$2thub 606 $aProcessament d'imatges$2thub 606 $aIntel·ligència artificial$2thub 608 $aLlibres electrònics$2thub 615 0$aInformation storage and retrieval systems. 615 0$aImage processing. 615 0$aArtificial intelligence$xData processing. 615 14$aInformation Storage and Retrieval. 615 24$aImage Processing. 615 24$aData Science. 615 7$aSistemes d'informació 615 7$aProcessament d'imatges 615 7$aIntel·ligència artificial 676 $a621.367 700 $aZhang$b Zheng$c(College teacher),$01768607 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910865233003321 996 $aBinary Representation Learning on Visual Images$94230432 997 $aUNINA