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Semantic-Based Image Retrieval（基于语义的图像检索）
  • 书号：9787030494900
    作者：刘颖
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  • 装帧：平装
    开本：B5
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    语种：en
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  • 定价： ￥98.00元
    售价： ￥77.42元
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目录

  • Contents
    Preface List of Abbreviations
    Chapter 1 Introduction 1
    1.1 Background 1
    1.1.1 The ‘Semantic Gap’ 1
    1.1.2 Query by Keywords 2
    1.2 Objectives 3
    1.3 Contributions of this Book 4
    1.3.1 Identifying Existing Semantic Learning Techniques 4
    1.3.2 Designing Effective Feature Extraction Methods for Arbitrary-Shaped Regions 4
    1.3.3 High-Level Concept Learning Using Decision Tree 5
    1.3.4 Applying RBIR with Semantics to Web Image Search 5
    1.4 Organization of the Book 6
    Chapter 2 Key Techniques in Semantic-Based Image Retrieval 8
    2.1 Introduction 8
    2.2 Techniques and Issues in Region-Based Image Retrieval 8
    2.2.1 Image Segmentation8
    2.2.2 Low-Level Image Feature Extraction 9
    2.2.3 Similarity Measure15
    2.2.4 Test Database and Performance Evaluation18
    2.3 High-Level Image Semantic Learning Techniques22
    2.3.1 Object-Ontology22
    2.3.2 Machine Learning25
    2.3.3 Relevance Feedback (RF)31
    2.3.4 Semantic Template34
    2.3.5 Fusion of Multiple Resources for Web Image Search36
    2.3.6 Deep Learning37
    2.3.7 Summary of Existing Techniques in Image Semantic Learning38
    2.4 Research Problems Addressed in this Book39
    Chapter 3 Deriving Image Semantics from Color Features.41
    3.1Introduction41
    3.2Region Color Feature Extraction and Semantic Color Naming41
    3.2.1 Region Color Features42
    3.2.2 Semantic Color Names45
    3.3 Image Retrieval using Semantic Color Names47
    3.3.1RBIR with Semantic Color Names48
    3.3.2 Feature Normalization48
    3.3.3 Image Similarity Measure using EMD50
    3.4 Results and Analysis52
    3.4.1 Test Database and Performance Evaluation Model52
    3.4.2 Comparison of Different Color Features53
    3.4.3 Performance of the Proposed Color Naming Method56
    3.4.4 Image Retrieval with Color Names, Region Color Features and Global Color Features56
    3.5 Discussion and Conclusions61
    Chapter 4 Effective Texture Feature Extraction from Arbitrary-Shaped Regions62
    4.1 Introduction62
    4.2 Deriving Texture Features from Arbitrary-Shaped Regions64
    4.2.1 Projection onto Convex Set (POCS) Theory64
    4.2.2 Extracting Region Texture Features Using POCS-ER64
    4.2.3 Theoretical Analysis of POCS-ER67
    4.2.4 Implementation of POCS-ER69
    4.3 POCS-ER on Brodatz Textures74
    4.3.1 Illustration of POCS-ER Process74
    4.3.2 Performance of POCS-ER Measured by PSNR78
    4.3.3 Performance of POCS-ER Measured by Retrieval Performance80
    4.4 POCS-ER for Real-World Image Retrieval.82
    4.4.1 Experimental Setups82
    4.4.2 Performance of Different Texture Feature Extraction Methods in RBIR83
    4.4.3 RBIR with Color, Texture, Color & Texture92
    4.4.4 Comparison of Region Features and Global Features in Image Retrieval94
    4.5 Conclusions and Discussion96
    Chapter 5 Deriving High-Level Image Concepts Using Decision Tree Learning97
    5.1 Introduction97
    5.2 Decision Tree Learning99
    5.2.1 Overview99
    5.2.2 Decision Tree Induction for Image Semantic Learning100
    5.3 The Proposed Decision Tree Induction Algorithm DT-ST101
    5.3.1 Semantic Template Construction102
    5.3.2 Image Feature Discretization104
    5.3.3 Decision Tree Induction106
    5.4 Results and Analysis115
    5.4.1 Selection of Pre-pruning Threshold116
    5.4.2 Pruning Unknowns117
    5.4.3 Handling Queries with Concepts outside the Training Concept Set118
    5.4.4 Comparison of DT-ST with ID3 and C4.5 119
    5.5 Region-Based Image Retrieval with High-Level Semantics120
    5.6 Discussion124
    5.6.1 Scalability of DT-ST124
    5.6.2 The Advantage of Image Retrieval with High-Level Concepts125
    5.7 Conclusions126
    Chapter 6 Application of Semantic-Based RBIR to Web Image Search127
    6.1 Introduction127
    6.2 The False Filtering Algorithm129
    6.3 Results and Analysis130
    6.3.1 Web Image Collection and Performance Evaluation130
    6.3.2 Experimental Results130
    6.4 Discussions138
    6.4.1 Integration138
    6.4.2 FF Response Time141
    6.4.3 Scalability143
    6.5 Conclusions143
    Chapter 7 Conclusions and Future Work145
    7.1 Conclusions of this Book145
    7.2 Future Research Directions147
    Bibliography149
    Appendix A HSV Color Histogram and HSV-RGB Conversion158
    Appendix B Tamura Texture Features 160
    Appendix C Illustration of POCS-ER Process Using ZR and MP162
    Appendix D Pre-pruning &Post-pruning in DT-ST167
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