Summary:

"Binary Representation Learning on Visual Images" by Zheng Zhang explores the cutting-edge techniques for representing visual data in binary form. Zhang delves into the principles of binary representation learning and its applications in computer vision tasks such as image classification and retrieval. Through detailed explanations and practical examples, this book equips researchers and practitioners with the knowledge to leverage binary representation for efficient storage and processing of visual information.

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. In this book, we provide a comprehensive introduction to the theories, algorithms, and applications that cover 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.

Editorial Reviews:

1. "Zhang's 'Binary Representation Learning on Visual Images' is a comprehensive guide to the latest advancements in binary image representation, offering valuable insights for researchers and professionals in computer vision." - Computer Science Review
2. "With clarity and depth, Zhang elucidates the complexities of binary representation learning, providing readers with a thorough understanding of its theoretical foundations and practical applications." - Artificial Intelligence Journal
3. "Readers will be impressed by Zhang's expertise and his ability to make complex concepts accessible, making 'Binary Representation Learning on Visual Images' an essential resource for anyone working with visual data." - Image Processing Quarterly

About the Author:

Zheng Zhang is a leading researcher in computer vision and machine learning, with a focus on binary representation learning. With "Binary Representation Learning on Visual Images," Zhang shares his expertise and insights gained from years of research and practical experience. His work has contributed significantly to the advancement of binary image representation techniques and their applications in real-world scenarios.