Machine Learning and Statistical Modeling Approaches to Image Retrieval
(Sprache: Englisch)
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...
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Klappentext zu „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.
Inhaltsverzeichnis zu „Machine Learning and Statistical Modeling Approaches to Image Retrieval “
PrefaceAcknowledgments1: Introduction1. Text-Based Image Retrieval2. Content-Based Image Retrieval 3. Automatic Linguistic Indexing of Images4. Applications of Image Indexing and Retrieval4.1 Web-Related Applications 4.2 Biomedical Applications4.3 Space Science4.4 Other Applications 5. Contributions of the Book5.1 A Robust Image Similarity Measure5.2 Clustering-Based Retrieval5.3 Learning and Reasoning with Regions5.4 Automatic Linguistic Indexing5.5 Modeling Ancient Paintings 6.The Structure of the Book2: Image Retrieval And Linguistic Indexing1. Introduction2. Content-Based Image Retrieval 2.1 Similarity Comparison2.2 Semantic Gap 3. Categorization and Linguistic Indexing4. Summary3: Machine Learning And Statistical Modeling 1. Introduction2. Spectral Graph Clustering3. VC Theory and Support Vector Machines 3.1 VC Theory3.2 Support Vector Machines4. Additive Fuzzy Systems5. Support Vector Learning for Fuzzy Rule-Based Classification Systems5.1 Additive Fuzzy Rule-Based Classification Systems5.2 Positive Definite Fuzzy Classifiers 5.3 An SVM Approach to Build Positive Definite Fuzzy Classifiers6. 2-D Multi-Resolution Hidden Markov Models7. Summary4: A Robust Region-Based Similarity Measure1. Introduction2. Image Segmentation and Representation2.1 Image Segmentation 2.2 Fuzzy Feature Representation of an Image2.3 An Algorithmic View 3. Unified Feature Matching3.1 Similarity Between Regions3.2 Fuzzy Feature Matching3.3 The UFM Measure3.4 An Algorithmic View4. An Algorithmic Summarization of the System 5. Experiments 5.1 Query Examples5.2 Systematic Evaluation 5.2.1 Experiment Setup5.2.2 Performance on Retrieval Accuracy5.2.3 Robustness to Segmentation Uncertainties5.3 Speed5.4 Comparison of Membership Functions6. Summary5: Cluster-Based Retrieval By Unsupervised Learning 1. Introduction 2. Retrieval of Similarity Induced Image Clusters2.1 System Overview 2.2 Neighboring Target Images Selection2.3 Spectral Graph Partitioning2.4 Finding a Representative Image for a
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Cluster3. An Algorithmic View3.1 Outline of Algorithm3.2 Organization of Clusters3.3 Computational Complexity3.4 Parameters Selection4. A Content-Based Image Clusters Retrieval System5. Experiments5.1 Query Examples5.2 Systematic Evaluation5.2.1 Measuring the Quality of Image Clustering5.2.2 Retrieval Accuracy5.3 Speed 5.4 Application of CLUE to Web Image Retrieval6. Summary6: Categorization By Learning And Reasoning With Regions1. Introduction2. Learning Region Prototypes Using Diverse Density2.1 Diverse Density 2.2 Learning Region Prototypes2.3 An Algorithmic View3. Categorization by Reasoning with Region Prototypes3.1 A Rule-Based Image Classifier3.2 Support Vector Machine Concept Learning 3.3 An Algorithmic View 4. Experiments 4.1 Experiment Setup 4.2 Categorization Results4.3 Sensitivity to Image Segmentation 4.4 Sensitivity to the Number of Categories4.5 Sensitivity to the Size and Diversity of Training Set 4.6 Speed 5. Summary 7: Automatic Linguistic Indexing Of Pictures 1. Introduction2. 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 Concepts5.2 Performance with a Controlled Database5.3 Categorization and Annotation Results6. 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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Bibliographische Angaben
- Autoren: Yixin Chen , Jia Li , James Z. Wang
- 2004, 2004, 182 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer Netherlands
- ISBN-10: 1402080344
- ISBN-13: 9781402080340
- Erscheinungsdatum: 27.05.2004
Sprache:
Englisch
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