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Learning-Based Local Visual Representation and Indexing.

By: Contributor(s): Material type: TextTextSeries: Computer Science Reviews and Trends SeriesPublisher: San Diego : Elsevier Science & Technology, 2015Copyright date: ©2015Edition: 1st edDescription: 1 online resource (128 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780128026205
Subject(s): Genre/Form: Additional physical formats: Print version:: Learning-Based Local Visual Representation and IndexingDDC classification:
  • 006.37
LOC classification:
  • TA1634 -- .L437 2015eb
Online resources:
Contents:
Front Cover -- Learning-Based Local Visual Representation and Indexing -- Copyright -- Contents -- Preface -- List of Figures -- List of Tables -- List of Algorithms -- Chapter 1: Introduction -- 1.1 Background and Significance -- 1.2 Literature Review of the Visual Dictionary -- 1.2.1 Local Interest-Point Extraction -- 1.2.2 Visual-Dictionary Generation and Indexing -- 1.3 Contents of This Book -- Chapter 2: Interest-Point Detection: Beyond Local Scale -- 2.1 Introduction -- 2.2 Difference of Contextual Gaussians -- 2.2.1 Local Interest-Point Detection -- 2.2.2 Accumulating Contextual Gaussian Difference -- 2.3 Mean Shift-Based Localization -- 2.3.1 Localization Algorithm -- 2.3.2 Comparison to Saliency -- 2.4 Detector Learning -- 2.5 Experiments -- 2.5.1 Database and Evaluation Criteria -- 2.5.2 Detector Repeatability -- 2.5.3 CASL for Image Search and Classification -- 2.6 Summary -- Chapter 3: Unsupervised Dictionary Optimization -- 3.1 Introduction -- 3.2 Density-Based Metric Learning -- 3.2.1 Feature-Space Density-Field Estimation -- 3.2.2 Learning a Metric for Quantization -- 3.3 Chain-Structure Recognition -- 3.3.1 Chain Recognition in Dictionary Hierarchy -- 3.4 Dictionary Transfer Learning -- 3.4.1 Cross-database Case -- 3.4.2 Incremental Transfer -- 3.5 Experiments -- 3.5.1 Quantitative results -- 3.6 Summary -- Chapter 4: Supervised Dictionary Learning via Semantic Embedding -- 4.1 Introduction -- 4.2 Semantic Labeling Propagation -- 4.2.1 Density Diversity Estimation -- 4.3 Supervised Dictionary Learning -- 4.3.1 Generative Modeling -- 4.3.2 Supervised Quantization -- 4.4 Experiments -- 4.4.1 Database and Evaluations -- 4.4.2 Quantitative Results -- 4.5 Summary -- Chapter 5: Visual Pattern Mining -- 5.1 Introduction -- 5.2 Discriminative 3D Pattern Mining -- 5.2.1 The Proposed Mining Scheme -- 5.2.2 Sparse Pattern Coding.
5.3 CBoP for Low Bit Rate Mobile Visual Search -- 5.4 Quantitative Results -- 5.4.1 Data Collection -- 5.4.2 Evaluation Criteria -- 5.4.3 Baselines -- 5.4.4 Quantitative Performance -- 5.5 Conclusion -- Conclusions -- References.
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Front Cover -- Learning-Based Local Visual Representation and Indexing -- Copyright -- Contents -- Preface -- List of Figures -- List of Tables -- List of Algorithms -- Chapter 1: Introduction -- 1.1 Background and Significance -- 1.2 Literature Review of the Visual Dictionary -- 1.2.1 Local Interest-Point Extraction -- 1.2.2 Visual-Dictionary Generation and Indexing -- 1.3 Contents of This Book -- Chapter 2: Interest-Point Detection: Beyond Local Scale -- 2.1 Introduction -- 2.2 Difference of Contextual Gaussians -- 2.2.1 Local Interest-Point Detection -- 2.2.2 Accumulating Contextual Gaussian Difference -- 2.3 Mean Shift-Based Localization -- 2.3.1 Localization Algorithm -- 2.3.2 Comparison to Saliency -- 2.4 Detector Learning -- 2.5 Experiments -- 2.5.1 Database and Evaluation Criteria -- 2.5.2 Detector Repeatability -- 2.5.3 CASL for Image Search and Classification -- 2.6 Summary -- Chapter 3: Unsupervised Dictionary Optimization -- 3.1 Introduction -- 3.2 Density-Based Metric Learning -- 3.2.1 Feature-Space Density-Field Estimation -- 3.2.2 Learning a Metric for Quantization -- 3.3 Chain-Structure Recognition -- 3.3.1 Chain Recognition in Dictionary Hierarchy -- 3.4 Dictionary Transfer Learning -- 3.4.1 Cross-database Case -- 3.4.2 Incremental Transfer -- 3.5 Experiments -- 3.5.1 Quantitative results -- 3.6 Summary -- Chapter 4: Supervised Dictionary Learning via Semantic Embedding -- 4.1 Introduction -- 4.2 Semantic Labeling Propagation -- 4.2.1 Density Diversity Estimation -- 4.3 Supervised Dictionary Learning -- 4.3.1 Generative Modeling -- 4.3.2 Supervised Quantization -- 4.4 Experiments -- 4.4.1 Database and Evaluations -- 4.4.2 Quantitative Results -- 4.5 Summary -- Chapter 5: Visual Pattern Mining -- 5.1 Introduction -- 5.2 Discriminative 3D Pattern Mining -- 5.2.1 The Proposed Mining Scheme -- 5.2.2 Sparse Pattern Coding.

5.3 CBoP for Low Bit Rate Mobile Visual Search -- 5.4 Quantitative Results -- 5.4.1 Data Collection -- 5.4.2 Evaluation Criteria -- 5.4.3 Baselines -- 5.4.4 Quantitative Performance -- 5.5 Conclusion -- Conclusions -- References.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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