Cover image for Learning-Based Local Visual Representation and Indexing

Learning-Based Local Visual Representation and Indexing

Author Qionghai Dai , Hongxun Yao , Ling-Yu Duan , Yue Gao , Rongrong Ji
Release Date: 2015/04/01
ISBN: 9780128024096
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Book Description

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques.
  • Discusses state-of-the-art procedures in learning-based local visual representation.
  • Shows how to master the basic techniques needed for building a large-scale visual search engine and indexing system
  • Provides insight into how machine learning techniques can be leveraged to refine the visual recognition system, especially in the part of visual feature representation.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Preface
  6. List of Figures
  7. List of Tables
  8. List of Algorithms
  9. Chapter 1: Introduction
    1. Abstract
    2. 1.1 Background and Significance
    3. 1.2 Literature Review of the Visual Dictionary
    4. 1.3 Contents of this Book
  10. Chapter 2: Interest-Point Detection: Beyond Local Scale
    1. Abstract
    2. 2.1 Introduction
    3. 2.2 Difference of Contextual Gaussians
    4. 2.3 Mean Shift-Based Localization
    5. 2.4 Detector Learning
    6. 2.5 Experiments
    7. 2.6 Summary
  11. Chapter 3: Unsupervised Dictionary Optimization
    1. Abstract
    2. 3.1 Introduction
    3. 3.2 Density-Based Metric Learning
    4. 3.3 Chain-Structure Recognition
    5. 3.4 Dictionary Transfer Learning
    6. 3.5 Experiments
    7. 3.6 Summary
  12. Chapter 4: Supervised Dictionary Learning via Semantic Embedding
    1. Abstract
    2. 4.1 Introduction
    3. 4.2 Semantic Labeling Propagation
    4. 4.3 Supervised Dictionary Learning
    5. 4.4 Experiments
    6. 4.5 Summary
  13. Chapter 5: Visual Pattern Mining
    1. Abstract
    2. 5.1 Introduction
    3. 5.2 Discriminative 3D Pattern Mining
    4. 5.3 CBoP for Low Bit Rate Mobile Visual Search
    5. 5.4 Quantitative Results
    6. 5.5 Conclusion
  14. Conclusions
  15. References
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