CiNii Books - Machine learning and statistical modeling approaches to image retrieval

In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentially be applied to many other areas, including biomedicine, space science, biometric identification, digital libraries, the military, education, commerce, culture and entertainment. Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well.

Table of Contents

Preface Acknowledgments 1: Introduction 1. Text-Based Image Retrieval 2. Content-Based Image Retrieval 3. Automatic Linguistic Indexing of Images 4. Applications of Image Indexing and Retrieval 4.1 Web-Related Applications 4.2 Biomedical Applications 4.3 Space Science 4.4 Other Applications 5. Contributions of the Book 5.1 A Robust Image Similarity Measure 5.2 Clustering-Based Retrieval 5.3 Learning and Reasoning with Regions 5.4 Automatic Linguistic Indexing 5.5 Modeling Ancient Paintings 6.The Structure of the Book 2: Image Retrieval And Linguistic Indexing 1. Introduction 2. Content-Based Image Retrieval 2.1 Similarity Comparison 2.2 Semantic Gap 3. Categorization and Linguistic Indexing 4. Summary 3: Machine Learning And Statistical Modeling 1. Introduction 2. Spectral Graph Clustering 3. VC Theory and Support Vector Machines 3.1 VC Theory 3.2 Support Vector Machines 4. Additive Fuzzy Systems 5. Support Vector Learning for Fuzzy Rule-Based Classification Systems 5.1 Additive Fuzzy Rule-Based Classification Systems 5.2 Positive Definite Fuzzy Classifiers 5.3 An SVM Approach to Build Positive Definite Fuzzy Classifiers 6. 2-D Multi-Resolution Hidden Markov Models 7. Summary 4: A Robust Region-Based Similarity Measure 1. Introduction 2. Image Segmentation and Representation 2.1 Image Segmentation 2.2 Fuzzy Feature Representation of an Image 2.3 An Algorithmic View 3. Unified Feature Matching 3.1 Similarity Between Regions 3.2 Fuzzy Feature Matching 3.3 The UFM Measure 3.4 An Algorithmic View 4. An Algorithmic Summarization of the System 5. Experiments 5.1 Query Examples 5.2 Systematic Evaluation 5.2.1 Experiment Setup 5.2.2 Performance on Retrieval Accuracy 5.2.3 Robustness to Segmentation Uncertainties 5.3 Speed 5.4 Comparison of Membership Functions 6. Summary 5: Cluster-Based Retrieval By Unsupervised Learning 1. Introduction 2. Retrieval of Similarity Induced Image Clusters 2.1 System Overview 2.2 Neighboring Target Images Selection 2.3 Spectral Graph Partitioning 2.4 Finding a Representative Image for a Cluster 3. An Algorithmic View 3.1 Outline of Algorithm 3.2 Organization of Clusters 3.3 Computational Complexity 3.4 Parameters Selection 4. A Content-Based Image Clusters Retrieval System 5. Experiments 5.1 Query Examples 5.2 Systematic Evaluation 5.2.1 Measuring the Quality of Image Clustering 5.2.2 Retrieval Accuracy 5.3 Speed 5.4 Application of CLUE to Web Image Retrieval 6. Summary 6: Categorization By Learning And Reasoning With Regions 1. Introduction 2. Learning Region Prototypes Using Diverse Density 2.1 Diverse Density 2.2 Learning Region Prototypes 2.3 An Algorithmic View 3. Categorization by Reasoning with Region Prototypes 3.1 A Rule-Based Image Classifier 3.2 Support Vector Machine Concept Learning 3.3 An Algorithmic View 4. Experiments 4.1 Experiment Setup 4.2 Categorization Results 4.3 Sensitivity to Image Segmentation 4.4 Sensitivity to the Number of Categories 4.5 Sensitivity to the Size and Diversity of Training Set 4.6 Speed 5. Summary 7: Automatic Linguistic Indexing Of Pictures 1. Introduction 2. System Architecture 2.1 Feature Extraction 2.2 Multiresolution Statistical Modeling 2.3 Statistical Linguistic Indexing 2.4 Major Advantages 3. Model-Based Learning of Concepts 4. Automatic Linguistic Indexing of Pictures 5. Experiments 5.1 Training Concepts 5.2 Performance with a Controlled Database 5.3 Categorization and Annotation Results 6. Summary 8: Modeling Ancient Paintings 1. Introduction 2. Mixture of 2-D Multi-Resolution Hidden Markov Models 3. Feature Extraction 4. Syste

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Machine learning and statistical modeling approaches to image retrieval

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