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Jingdong book

智能图像检索关键技术研究

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Jingdong book

智能图像检索关键技术研究

Name: 智能图像检索关键技术研究
Brand: Jingdong book
SKU: 3118212361
Price: 48.45 USD
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《智能图像检索关键技术研究》是毛金莲老师多年从事多媒体信息检索研究工作的系统性总结，包含了她对当前国际、国内在智能图像检索关键技术研究上的个人分析与展望。对智能图像检索中涉及的关键技术，如图像特征表达技术、图像相似性度量技术、图像索引技术和图像检索结果评价技术等，按照智能图像检索技术的发展脉络，借助研究成果，逐一进行了详细的介绍和总结归纳。
《智能图像检索关键技术研究》在对智能图像检索关键技术进行介绍时，提供了大量制作精美的算法流程图和示意图。对近年来智能图像检索中关键技术进行了比较系统的梳理和介绍，对于从事多媒体信息检索，尤其是智能图像检索研究方向的相关研究者，具有非常好的学术参考价值。与此同时，《智能图像检索关键技术研究》对部分重要算法还提供了相应的程序伪代码或源代码，同时还给出了一些图像检索相关的网络开源资源信息，这对于从事该领域的工程人员，具有非常好的工作实用价值。

Author Description

毛金莲，女，浙江大学工程硕士，现就职于浙江商业职业技术学院，讲师职称。长期以来专注于计算机图像处理与多媒体信息检索领域的研究，具有非常坚实的科研理论水平和丰富的科研实践能力，在网络图像检索方向上取得理论创新，提出一种保持几何与语义相似性的非负矩阵图像检索算法和一种自适应多视角学习的图像检索算法。前者扩展了传统非负矩阵算法模型，在多媒体信息检索中实现对几何相似性与语义相似性的保持；后者将L1图的鲁棒性和自适应性成功地引入传统多视角学习模型中，使之抗噪声能力*强，对数据具有自适应性。近三年里，主持和参与了多项横向与纵向科研课题研究，比如浙江省教育厅项目“稀疏多视角特征选择在海量网络图像标注与检索中的应用”，浙江省旅游厅项目“基于移动互联技术的民族风情旅游营销研究”。在国内及国际学术会议和学术期刊上陆续发表了若干高水平研究论文。主编和参编了多部教学教材，包括浙江大学出版社出版的《大学信息技术案例教程》和高等教育出版社出版的《大学信息技术基础实训教程》等。此外，还多次指导学生参加各类学科竞赛活动与新苗人才项目，获得浙江省大学生电子商务大赛一等奖和全国大学生三创大赛二等奖和创意奖等，成绩斐然。

Catalogue

第1章绪论
1.1 研究背景
1.2 核心研究问题及挑战
1.3 课题研究的意义
参考文献

第2章智能图像检索关键技术概述
2.1 图像特征提取与表达
2.1.1 基于文本描述的图像特征
2.1.2 基于图像内容的视觉特征
2.1.3 基于图像高层语义的特征
2.2 图像相似度度量
2.2.1 L1距离／L2距离和Lp距离
2.2.2 直方图相交距离
2.2.3 二次式距离
2.2.4 马氏距离
2.2.5 非几何的相似度方法
2.3 图像索引技术
2.3.1 基于颜色特征的索引技术
2.3.2 基于形状特征的索引技术
2.3.3 基于纹理特征的索引技术
2.3.4 基于对象空间关系的索引技术
2.3.5 多维图像特征的索引技术
2.4 图像检索评测指标
2.5 图像评测数据集
2.5.1 Caltech256
2.5.2 Corel-5K图像数据库
2.5.3 MSRA-MM2.0多媒体数据集
2.5.4 NUS-WIDE图像数据集
2.6 本章小结
参考文献

第3章基于文本的图像检索技术
3.1 基于文本的图像检索简介
3.2 图像索引数据库的构建
3.3 关键字的匹配查找
3.3.1 单关键字匹配算法
3.3.2 多关键字匹配算法
3.4 基于文本的图像检索的问题与挑战
3.5 本章小结
参考文献

第4章基于内容的图像检索技术
4.1 一种保持视觉和语义相似性的非负矩阵分解及其在图像检索中的应用
4.1.1 NMF算法回顾
4.1.2 VSNMF算法
4.1.3 实验测试
4.1.4 实验结论
4.2 自适应多视图学习算法及其在基于内容的图像检索中的应用
4.2.1 相关工作
4.2.2 自适应多视角学习算法
4.2.3 实验分析
4.2.4 实验结论
4.3 基于多特征选择的图像检索算法
4.3.1 多轮廓特征提取
4.3.2 多轮廓特征选择
4.3.3 实验测试
4.4 本章小结
参考文献

第5章基于语义的图像检索技术
5.1 图像内容层次结构
5.2 语义鸿沟问题
5.3 图像语义提取方法
5.3.1 利用系统知识的语义提取
5.3.2 基于系统交互的语义生成
5.3.3 基于外部信息的语义提取
5.4 图像自动语义标注方法
5.4.1 图像自动语义标注方法
5.4.2 基于稀疏典型性相关分析的图像标注方法
5.5 本章小结
参考文献

第6章面向特定平台的图像检索技术
6.1 面向微机与服务器的图像检索技术
6.2 面向Intemet的图像检索技术
6.3 面向智能移动终端的图像检索技术
6.4 面向分布式计算和存储平台的图像检索技术
6.5 本章小结
参考文献

第7章针对特定应用的图像检索技术
7.1 针对特定自然图像的图像检索技术
7.2 针对情感语义图像的图像检索技术
7.2.1 图像情感信息（Ixnage emotion information）
7.2.2 图像情感标注（Image emotion labeling）
7.2.3 基于情感语义的图像检索系统的研究
7.3 本章小结
参考文献

第8章智能图像检索技术研究展望
附录A 图像相关开源工具包资源
A1 开源图像处理工具包
A1.1 OpenCV
A1.2 EmguCV
A1.3 Image ProcessingToolbox
A1.4 VLFeat
A2 开源图像检索引擎
A2.1 GRire
A2.2 Caliph&Emir
A2.3 Apache Lucene
A2.4 Nutch
A2.5 Egothor
A2.6 BDDBot
A2.7 Zilverline
A2.8 Constellio
A2.9 起点R3企业级搜索引擎
A2.10 Kaaa分布式Lucene
附录B 部分重要算法伪／源码
B1 Boyer-Moore（BM）关键字匹配算法
B2 Wu-Manber多关键字匹配算法
B3 颜色空间转换函数
B4 PCA主成分分析代码
B5 AMVL自适应多视角学习算法代码
B6 Sparse CCA稀疏典型性相关分析
作者简介

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