03787nam a22005415i 4500 978-981-97-2112-2 DE-He213 20251030112304.0 cr nn 008mamaa 240608s2024 si | s |||| 0|eng d 9789819721122 978-981-97-2112-2 10.1007/978-981-97-2112-2 doi QA75.5-76.95 UNH bicssc UND bicssc COM030000 bisacsh UNH thema UND thema Zhang, Zheng. author. (orcid)0000-0003-1470-6998 https://orcid.org/0000-0003-1470-6998 aut http://id.loc.gov/vocabulary/relators/aut 66091 Binary Representation Learning on Visual Images [electronic resource] : Learning to Hash for Similarity Search / by Zheng Zhang. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVI, 200 p. 45 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. Information storage and retrieval systems. 65568 Image processing. Artificial intelligence Data processing. Information Storage and Retrieval. 65570 Image Processing. Data Science. SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819721115 Printed edition: 9789819721139 Printed edition: 9789819721146 https://doi.org/10.1007/978-981-97-2112-2 ZDB-2-SCS ZDB-2-SXCS 60617 60617