Binary Representation Learning on Visual Images: Learning to Hash for Similarity Search

Zhang, Zheng

Binary Representation Learning on Visual Images: Learning to Hash for Similarity Search
  • 出版商: Springer
  • 出版日期: 2024-06-09
  • 售價: $7,150
  • 貴賓價: 9.5 折 $6,793
  • 語言: 英文
  • 頁數: 200
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819721113
  • ISBN-13: 9789819721115

    • 海外代購書籍(需單獨結帳)

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商品描述

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.


商品描述(中文翻譯)

本書介紹了視覺影像中二進位表示學習的開創性發展，這是一種在機器學習和多媒體領域內的尖端數據轉換方法。二進位表示學習，通常被稱為學習哈希或哈希，擅長將高維數據轉換為緊湊的二進位碼，同時保留語義屬性並維持相似性度量。

本書全面介紹了基於哈希的視覺影像檢索的最新研究，重點在於二進位表示。這些表示對於在大規模數據上實現快速且可靠的特徵提取和相似性評估至關重要。本書深入分析了視覺影像的二進位表示學習中各種研究方法，涵蓋了基礎淺層哈希、高階相似性保留哈希、深度哈希，以及對抗性和穩健的深度哈希技術。這些方法能夠幫助讀者熟練掌握傳統與尖端二進位表示、建模和學習的基本原則。本書中闡述的二進位表示學習理論和方法論將對來自機器學習、多媒體、社交網絡分析、網頁搜尋、資訊檢索、數據挖掘等多個領域的讀者有所裨益。

作者簡介

Zheng Zhang is a full Professor at School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China. He is the deputy director of the Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, China. Dr. Zhang's research interests mainly focus on multimedia content analysis and understanding, especially multimedia retrieval, multi-modal learning, and big data mining. He has published more than 100 technical papers in prestigious international journals and conference proceedings, with over 7,000 citations according to Google Scholar (h-Index: 40). He is a co-recipient of paper awards in ACM Multimedia Asia'21, EAI ICMTEL'22, and SMARTCOMP'14. He was the recipient of the CAAI Outstanding Young Research Achievement Award and has also been featured in the 'World's Top2% Scientists' for consecutive years. He serves as the Editorial Board Member of IEEE Trans. on Affective Computing (IEEE TAC), IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), and Elsevier Information Fusion (INFFUS), as well as the Area Chair of ICML, CVPR, ACM MM, and others. He is an IEEE and CCF Senior Member.


作者簡介(中文翻譯)

鄭章是中國哈爾濱工業大學深圳校區計算機科學與技術學院的全職教授。他是深圳視覺物體檢測與識別重點實驗室的副主任。鄭博士的研究興趣主要集中在多媒體內容分析與理解，特別是多媒體檢索、多模態學習和大數據挖掘。他在國際知名期刊和會議論文集中發表了超過100篇技術論文，根據Google Scholar的資料引用次數超過7,000次（h-Index: 40）。他是ACM Multimedia Asia'21、EAI ICMTEL'22和SMARTCOMP'14論文獎的共同獲獎者。他曾獲得CAAI傑出青年研究成就獎，並連續多年入選「全球前2%科學家」。他擔任IEEE情感計算期刊（IEEE TAC）、IEEE生物醫學與健康信息學期刊（IEEE JBHI）和Elsevier信息融合（INFFUS）的編輯委員會成員，以及ICML、CVPR、ACM MM等會議的區域主席。他是IEEE和CCF的資深會員。

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