Binary Representation Learning on Visual Images : Learning to Hash for Similarity Search / by Zheng Zhang.

Format:

Book

Author/Creator:

Zhang, Zheng (College teacher), author.

Language:

English

Subjects (All):

Information storage and retrieval systems.

Image processing.

Artificial intelligence--Data processing.

Artificial intelligence.

Information Storage and Retrieval.

Image Processing.

Data Science.

Local Subjects:

Information Storage and Retrieval.

Image Processing.

Data Science.

Physical Description:

1 online resource (210 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Zhang, Zheng Binary Representation Learning on Visual Images

ISBN:

9789819721122