Machine Learning and Statistical Modeling Approaches to Image Retrieval (Paperback, Softcover Repri)

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