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Learning-Based Local Visual Representation and Indexing - 1st Edition - ISBN: 9780128024096, 9780128026205

Learning-Based Local Visual Representation and Indexing

1st Edition

Authors: Rongrong Ji Yue Gao Ling-Yu Duan Hongxun Yao Qionghai Dai
Paperback ISBN: 9780128024096
eBook ISBN: 9780128026205
Imprint: Morgan Kaufmann
Published Date: 23rd March 2015
Page Count: 128
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Table of Contents

  • 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.

Key Features

  • 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.


No. of pages:
© Morgan Kaufmann 2015
23rd March 2015
Morgan Kaufmann
Paperback ISBN:
eBook ISBN:

Ratings and Reviews

About the Authors

Rongrong Ji

Professor Rongrong Ji , is the Director of the Intelligent Multimedia Lab at Xiamen University. He is an active researcher pursuing innovations in multimedia, computer vision and pattern recognition. His scholarly work mainly focuses on building cutting-edge computer systems to understand visual scenes, to retrieve visual instances, and to digest human behaviors, with emerging applications to mobile visual search and social media analytics. His recent interests include compact visual descriptor, social media sentiment analysis, and holistic scene.

He has served as the guest editor of IEEE MultiMedia Magazine, Neurocomputing, Signal Processing, ACM Multimedia Systems, and the Journal of Multimedia Tools and Applications. In addition he has served as the technical session chair at numerous conferences including, International Conference on Multimedia Retrieval 2014, Visual Communications and Image Processing 2013 and the 2012 Pacific-Rim Conference on Multimedia, etc. He has over 900 Citations and a Google Scholar h-index of 15.

Affiliations and Expertise

Director of the Intelligent Multimedia Lab at Xiamen University,

Yue Gao

Yue Gao

Yue Gao is with the Department of Automation, Tsinghua University. His recent research focuses on the areas of neuroimaging, multimedia and remote sensing. He is a senior member of IEEE.

Affiliations and Expertise

Department of Automation, Tsinghua University, Beijing, China

Ling-Yu Duan

Affiliations and Expertise

Peking University

Hongxun Yao

Affiliations and Expertise

Harbin Institute of Technology, Harbin, China

Qionghai Dai

Qionghai Dai is with the Deparment of Automation, Tsinghua University. He has published more than 120 conference and journal papers, and holds 67 patents. His current research interests include the areas of computational photography, computational optical sensing, and compressed sensing imaging and vision. His work is motivated by challenging applications in the fields of computer vision, computer graphics, and robotics. He is a senior member of IEEE.

Affiliations and Expertise

Deparment of Automation, Tsinghua University, Beijing, China