| 期刊全稱 | Binary Representation Learning on Visual Images | | 期刊簡(jiǎn)稱 | Learning to Hash for | | 影響因子2023 | Zheng Zhang | | 視頻video | http://file.papertrans.cn/193/192679/192679.mp4 | | 發(fā)行地址 | Broadens the understanding of binary representation learning in the context of visual data.Offers the latest research trends in binary representation, modeling and learning.Expounds the potential, int | | 圖書(shū)封面 |  | | 影響因子 | .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 the | | Pindex | Book 2024 |
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