《基於語義的圖像檢索（英文版）》是2016年科學出版社出版的圖書，作者是劉穎。
基本介紹
- 中文名：基於語義的圖像檢索（英文版）
- 作者：劉穎
- 出版社：科學出版社
- ISBN：9787030494900
內容簡介,圖書目錄,
內容簡介
《基於語義的圖像檢索（英文版）》針對基於高層語義的圖像檢索的關鍵技術環節進行了介紹和論述。主要內容：（1）基於語義的圖像檢索技術的研究背景，以及圖像特徵提取，圖像相似度度量，圖像語義學習等各關鍵環節經典和現有算法的綜述介紹；（2）基於作者提出的一個基於區域的語義圖像檢索算法，闡述了如何實現基於語義的圖像檢索，如何提取有效的圖像數字特徵，如何從圖像數字特徵提取圖像語義，（3）將將所提出的基於語義的圖像檢索算法用於網路圖像檢索的改進，描述了其套用價值。
圖書目錄
Preface
List of Abbreviations
Chapter 1 Introduction
1．1 Background
1．1．1 The 'Semantic Gap
1．1．2 Query by Keywords
1．2 Objectives
1．3 Contributions of this Book
1．3．1 Identifying Existing Semantic Learning Techniques
1．3．2 Designing Effective Feature Extraction Methods for Arbitrary-Shaped Regions
1．3．3 High-Level Concept Learning Using Decision Tree
1．3．4 Applying RBIR with Semantics to Web Image Search
1．4 Organization of the Book
Chapter 2 Key Techniques in Semantic-Based Image Retrieval
2．1 Introduction
2．2 Techniques and Issues in Region-Based Image Retrieval
2．2．1 Image Segmentation
2．2．2 Low-Level Image Feature Extraction
2．2．3 Similarity Measure
2．2．4 Test Database and Performance Evaluation
2．3 High-Level Image Semantic Learning Techniques
2．3．1 Object-Ontology
2．3．2 Machine Learning
2．3．3 Relevance Feedback (RF)
2．3．4 Semantic Template
2．3．5 Fusion of Multiple Resources for Web Image Search
2．3．6 Deep Learning
2．3．7 Summary of Existing Techniques in Image Semantic Learning
2．4 Research Problems Addressed in this Book
Chapter 3 Deriving Image Semantics from Color Features
3．1 Introduction
3．2 Region Color Feature Extraction and Semantic Color Naming
3．2．1 Region Color Features
3．2．2 Semantic Color Names
3．3 Image Retrieval using Semantic Color Names
3．3．1 RBIR with Semantic Color Names
3．3．2 Feature Normalization
3．3．3 Image Similarity Measure using EMD
3．4 Results and Analysis
3．4．1 Test Database and Performance Evaluation Model
3．4．2 Comparison of Different Color Features
3．4．3 Performance of the Proposed Color Naming Method
3．4．4 Image Retrieval with Color Names， Region Color Features and Global
Color Features
3．5 Discussion and Conclusions
Chapter 4 Effective Texture Feature Extraction from Arbitrary-Shaped Regions
4．1 Introduction
4．2 Deriving Texture Features from Arbitrary-Shaped Regions
4．2．1 Projection onto Convex Set (POCS) Theory
4．2．2 Extracting Region Texture Features Using POCS-ER
4．2．3 Theoretical Analysis of POCS-ER
4．2．4 Implementation of POCS-ER
4．3 POCS-ER on Brodatz Textures
4．3．1 Illustration of POCS-ER Process
4．3．2 Performance of POCS-ER Measured by PSNR
4．3．3 Performance of POCS-ER Measured by Retrieval Performance
4．4 POCS-ER for Real-World Image Retrieval
4．4．1 Experimental Setups
4．4．2 Performance of Different Texture Feature Extraction Methods in RBIR．．．
4．4．3 RBIR with Color， Texture， Color & Texture
4．4．4 Comparison of Region Features and Global Features in Image Retrieval
4．5 Conclusions and Discussion
Chapter 5 Deriving High-Level Image Concepts Using Decision Tree Learning
5．1 Introduction
5．2 Decision Tree Learning
5．2．1 Overview
5．2．2 Decision Tree Induction for Image Semantic Learning
5．3 The Proposed Decision Tree Induction Algorithm DT-ST
5．3．1 Semantic Template Construction
5．3．2 Image Feature Discretization
5．3．3 Decision Tree Induction
5．4 Results and Analysis
5．4．1 Selection of Pre-pnming Threshold
5．4．2 Pruning Unknowns
5．4．3 Handling Queries with Concepts outside the Training Concept Set
5．4．4 Comparison of DT-ST with ID3 and C4．5
5．5 Region-Based Image Retrieval with High-Level Semantics
5．6 Discussion
5．6．1 Scalability of DT-ST
5．6．2 The Advantage of Image Retrieval with High-Level Concepts
5．7 Conclusions
Chapter 6 Application of Semantic-Based RBIR to Web Image Search
6．1 Introduction
6．2 The False Filtering Algorithm
6．3 Results and Analysis
6．3．1 Web Image Collection and Performance Evaluation
6．3．2 Experimental Results
6．4 Discussions
6．4．1 Integration
6．4．2 FF Response Time
6．4．3 Scalability
6．5 Conclusions
Chapter 7 Conclusions and Future Work
7．1 Conclusions of this Book
7．2 Future Research Directions
Bibliography
Appendix A HSV Color Histogram and HSV-RGB Conversion
Appendix B Tamura Texture Features
Appendix C lllustration of POCS-ER Process Using ZR and MP
Appendix D Pre-pruning &Post-pruning in DT-ST
