Learning-Based Local Visual Representation and Indexing

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

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.

Researchers, graduate students and academic faculty in the fields of artificial intelligence. More specifically, those who are doing research in computer vision, pattern recognition, multimedia technique, machine learning, and data mining.