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简介
任伟的《时空视频检索(英文版)》重点挖掘了视频的时空关系，探索
了利用机器学习的方法进行视频切割、语义分类。本书分七章，阐明了图
像的各种特性，论述了视频的特征，系统介绍了视频的时空逻辑关系、视
频的统计分析方法，研究了如何捕捉视频的时空特性，如何利用人工智能
神经网络进行视频切割，如何训练计算机“学会”用人类的思维进行视频
语义分类、检索。各章节撰写排列体现了从简到繁、由浅入深、从理论到
实际、从技术到系统的特点。
《时空视频检索(英文版)》可以作为高等学校信号与图像处理、计算
机科学、机器学习、人工智能、机器视觉等领域的研究生教材和参考书，
也可以作为在这些领域从事相关工作的高级科学技术人员的参考书。
目录
Chapter Ⅰ Introduction
1.1 Motivation
1.2 Proposed Solution
1.3 Structure of Book
Chapter Ⅱ Approaches to Video Retrieval
2.1 Introduction
2.2 Video Structure and Properties
2.3 Query
2.4 Similarity Metrics
2.5 Performance Evaluation Metrics
2.6 Systems
Chapter Ⅲ Spatio-temporal Image and Video Analysis
3.1 Spatio-temporal Information for Video Retrieval
3.2 Spatial Information Modelling in Multimedia Retrieval .
3.3 Temporal Model
3.4 Spatio-temporal Information Fusion
Chapter Ⅳ Video Spatio-temporal Analysis and Retrieval （VSTAR） :A New Model
4.1 VSTAR Model Components
4.2 Spatial Image Analysis
4.3 A Model for the Temporal Analysis of Image Sequences
4.4 Video Representation.Indexing.and Retrieval Usinz VSTAR
4.5 Conclusions
Chapter Ⅴ Two Comparison Baseline Models for Video Retrieval
5.1 Baseline Models
5.2 Adjeroh et al.（1999） Sequences Matching――Video Retrieval Model
5.3 Kim and Park （2002a） data set matching――Video Retrieval Model
Chapter Ⅵ Spatio-temporal Video Retrieval――Experiments and Results
6.1 Purpose of Experiments
6.2 Data Description
6.3 Spatial and Temporal Feature Extraction
6.4 Video Retrieval Models: Procedure for Parameter Optimisation
6.5 Video Retrieval Models:Resuhs on Parameter Optimisation
6.6 Comparison of Four Models
6.7 Model Robustness （Noise）
6.8 Computational Complexity
6.9 Conclusions
Chapter Ⅶ Conclusions
7.1 Reflections on the book as a whole
7.2 Support for book statement
7.3 Limitations of the spatio-temporal knowledge-based model
7.4 Directions for further work
Appendix A Compressed vs.Uncompressed Video
Appendix B Video Annotation
B.1 Semi-automatic Video Annotation System
B.2 Automatic Annotation by Object Tracking
Appendix C Object-pair Correlation Matrix
Appendix D Key-frames Extraction
D.1 Feature-based Representation and Similarity Measures .
D.2 Threshold Selection
Appendix E Audio Features
Reference
1.1 Motivation
1.2 Proposed Solution
1.3 Structure of Book
Chapter Ⅱ Approaches to Video Retrieval
2.1 Introduction
2.2 Video Structure and Properties
2.3 Query
2.4 Similarity Metrics
2.5 Performance Evaluation Metrics
2.6 Systems
Chapter Ⅲ Spatio-temporal Image and Video Analysis
3.1 Spatio-temporal Information for Video Retrieval
3.2 Spatial Information Modelling in Multimedia Retrieval .
3.3 Temporal Model
3.4 Spatio-temporal Information Fusion
Chapter Ⅳ Video Spatio-temporal Analysis and Retrieval （VSTAR） :A New Model
4.1 VSTAR Model Components
4.2 Spatial Image Analysis
4.3 A Model for the Temporal Analysis of Image Sequences
4.4 Video Representation.Indexing.and Retrieval Usinz VSTAR
4.5 Conclusions
Chapter Ⅴ Two Comparison Baseline Models for Video Retrieval
5.1 Baseline Models
5.2 Adjeroh et al.（1999） Sequences Matching――Video Retrieval Model
5.3 Kim and Park （2002a） data set matching――Video Retrieval Model
Chapter Ⅵ Spatio-temporal Video Retrieval――Experiments and Results
6.1 Purpose of Experiments
6.2 Data Description
6.3 Spatial and Temporal Feature Extraction
6.4 Video Retrieval Models: Procedure for Parameter Optimisation
6.5 Video Retrieval Models:Resuhs on Parameter Optimisation
6.6 Comparison of Four Models
6.7 Model Robustness （Noise）
6.8 Computational Complexity
6.9 Conclusions
Chapter Ⅶ Conclusions
7.1 Reflections on the book as a whole
7.2 Support for book statement
7.3 Limitations of the spatio-temporal knowledge-based model
7.4 Directions for further work
Appendix A Compressed vs.Uncompressed Video
Appendix B Video Annotation
B.1 Semi-automatic Video Annotation System
B.2 Automatic Annotation by Object Tracking
Appendix C Object-pair Correlation Matrix
Appendix D Key-frames Extraction
D.1 Feature-based Representation and Similarity Measures .
D.2 Threshold Selection
Appendix E Audio Features
Reference
Video retrieval using spatio-temporal information
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