Machine Learning and Statistical Modeling Approaches to Image Retrieval by Yixin Chen, Jia Li, James Z. Wang , Springer

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Springer Machine Learning and Statistical Modeling Approaches to Image Retrieval by Yixin Chen, Jia Li, James Z. Wang

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. _x000D_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._x000D_ Table of contents : - _x000D_ Preface_x000D_ Acknowledgments_x000D_ _x000D_ 1: Introduction_x000D_ 1. Text-Based Image Retrieval_x000D_ 2. Content-Based Image Retrieval _x000D_ 3. Automatic Linguistic Indexing of Images_x000D_ 4. Applications of Image Indexing and Retrieval_x000D_ 4.1 Web-Related Applications _x000D_ 4.2 Biomedical Applications_x000D_ 4.3 Space Science_x000D_ 4.4 Other Applications _x000D_ 5. Contributions of the Book_x000D_ 5.1 A Robust Image Similarity Measure_x000D_ 5.2 Clustering-Based Retrieval_x000D_ 5.3 Learning and Reasoning with Regions_x000D_ 5.4 Automatic Linguistic Indexing_x000D_ 5.5 Modeling Ancient Paintings _x000D_ 6.The Structure of the Book_x000D_ _x000D_ 2: Image Retrieval And Linguistic Indexing_x000D_ 1. Introduction_x000D_ 2. Content-Based Image Retrieval _x000D_ 2.1 Similarity Comparison_x000D_ 2.2 Semantic Gap _x000D_ 3. Categorization and Linguistic Indexing_x000D_ 4. Summary_x000D_ _x000D_ 3: Machine Learning And Statistical Modeling _x000D_ 1. Introduction_x000D_ 2. Spectral Graph Clustering_x000D_ 3. VC Theory and Support Vector Machines _x000D_ 3.1 VC Theory_x000D_ 3.2 Support Vector Machines_x000D_ 4. Additive Fuzzy Systems_x000D_ 5. Support Vector Learning for Fuzzy Rule-Based Classification Systems_x000D_ 5.1 Additive Fuzzy Rule-Based Classification Systems_x000D_ 5.2 Positive Definite Fuzzy Classifiers _x000D_ 5.3 An SVM Approach to Build Positive Definite Fuzzy Classifiers_x000D_ 6. 2-D Multi-Resolution Hidden Markov Models_x000D_ 7. Summary_x000D_ _x000D_ 4: A Robust Region-Based Similarity Measure_x000D_ 1. Introduction_x000D_ 2. Image Segmentation and Representation_x000D_ 2.1 Image Segmentation _x000D_ 2.2 Fuzzy Feature Representation of an Image_x000D_ 2.3 An Algorithmic View _x000D_ 3. Unified Feature Matching_x000D_ 3.1 Similarity Between Regions_x000D_ 3.2 Fuzzy Feature Matching_x000D_ 3.3 The UFM Measure_x000D_ 3.4 An Algorithmic View_x000D_ 4. An Algorithmic Summarization of the System _x000D_ 5. Experiments _x000D_ 5.1 Query Examples_x000D_ 5.2 Systematic Evaluation _x000D_ 5.2.1 Experiment Setup_x000D_ 5.2.2 Performance on Retrieval Accuracy_x000D_ 5.2.3 Robustness to Segmentation Uncertainties_x000D_ 5.3 Speed_x000D_ 5.4 Comparison of Membership Functions_x000D_ 6. Summary_x000D_ _x000D_ 5: Cluster-Based Retrieval By Unsupervised Learning _x000D_ 1. Introduction _x000D_ 2. Retrieval of Similarity Induced Image Clusters_x000D_ 2.1 System Overview _x000D_ 2.2 Neighboring Target Images Selection_x000D_ 2.3 Spectral Graph Partitioning_x000D_ 2.4 Finding a Representative Image for a Cluster_x000D_ 3. An Algorithmic View_x000D_ 3.1 Outline of Algorithm_x000D_ 3.2 Organization of Clusters_x000D_ 3.3 Computational Complexity_x000D_ 3.4 Parameters Selection_x000D_ 4. A Content-Based Image Clusters Retrieval System_x000D_ 5. Experiments_x000D_ 5.1 Query Examples_x000D_ 5.2 Systematic Evaluation_x000D_ 5.2.1 Measuring the Quality of Image Clustering_x000D_ 5.2.2 Retrieval Accuracy_x000D_ 5.3 Speed _x000D_ 5.4 Application of CLUE to Web Image Retrieval_x000D_ 6. Summary_x000D_ _x000D_ 6: Categorization By Learning And Reasoning With Regions_x000D_ 1. Introduction_x000D_ 2. Learning Region Prototypes Using Diverse Density_x000D_ 2.1 Diverse Density _x000D_ 2.2 Learning Region Prototypes_x000D_ 2.3 An Algorithmic View_x000D_ 3. Categorization by Reasoning with Region Prototypes_x000D_ 3.1 A Rule-Based Image Classifier_x000D_ 3.2 Support Vector Machine Concept Learning _x000D_ 3.3 An Algorithmic View _x000D_ 4. Experiments _x000D_ 4.1 Experiment Setup _x000D_ 4.2 Categorization Results_x000D_ 4.3 Sensitivity to Image Segmentation _x000D_ 4.4 Sensitivity to the Number of Categories_x000D_ 4.5 Sensitivity to the Size and Diversity of Training Set _x000D_ 4.6 Speed _x000D_ 5. Summary_x000D_ 7: Automatic Linguistic Indexing Of Pictures _x000D_ 1. Introduction_x000D_ 2. System Architecture _x000D_ 2.1 Feature Extraction _x000D_ 2.2 Multiresolution Statistical Modeling _x000D_ 2.3 Statistical Linguistic Indexing _x000D_ 2.4 Major Advantages _x000D_ 3. Model-Based Learning of Concepts _x000D_ 4. Automatic Linguistic Indexing of Pictures _x000D_ 5. Experiments _x000D_ 5.1 Training Concepts_x000D_ 5.2 Performance with a Controlled Database_x000D_ 5.3 Categorization and Annotation Results_x000D_ 6. Summary_x000D_ 8: Modeling Ancient Paintings _x000D_ 1. Introduction _x000D_ 2. Mixture of 2-D Multi-Resolution Hidden Markov Models _x000D_ 3. Feature Extraction _x000D_ 4. Syste_x000D_