基于语义的图像检索 科学出版社 rtf 下载 2025 kindle mobi lrf 极速 pdf

本书针对基于高层语义的图像检索的关键技术环节进行了介绍和论述。主要内容：（1）基于语义的图像检索技术的研究背景，以及图像特征提取，图像相似度度量，图像语义学习等各关键环节经典和现有算法的综述介绍；（2）基于作者提出的一个基于区域的语义图像检索算法，阐述了如何实现基于语义的图像检索，如何提取有效的图像数字特征，如何从图像数字特征提取图像语义，（3）将将所提出的基于语义的图像检索算法用于网络图像检索的改进，描述了其应用价值。

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

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