Sparse Representation, Modeling and Learning in Visual Recognition

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction and Fundamentals

   1. Front Matter

      Pages 1-1

      PDF

   2. Introduction

      • Hong Cheng

      Pages 3-19

   3. The Fundamentals of Compressed Sensing

      • Hong Cheng

      Pages 21-53

3. Sparse Representation, Modeling and Learning

   1. Front Matter

      Pages 55-55

      PDF

   2. Sparse Recovery Approaches

      • Hong Cheng

      Pages 57-90

   3. Robust Sparse Representation, Modeling and Learning

      • Hong Cheng

      Pages 91-115

   4. Efficient Sparse Representation and Modeling

      • Hong Cheng

      Pages 117-151

4. Visual Recognition Applications

   1. Front Matter

      Pages 153-153

      PDF

   2. Feature Representation and Learning

      • Hong Cheng

      Pages 155-181

   3. Sparsity-Induced Similarity

      • Hong Cheng

      Pages 183-200

   4. Sparse Representation and Learning-Based Classifiers

      • Hong Cheng

      Pages 201-211

5. Advanced Topics

   1. Front Matter

      Pages 213-213

      PDF

   2. Beyond Sparsity

      • Hong Cheng

      Pages 215-235

6. Back Matter

   Pages 237-257

   PDF

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

• Compressed Sensing
• Dictionary Learning
• Sparse Bayesian Learning
• Sparse Coding
• Sparse Representation
• Sparsity Induced Similarity
• Visual Recognition

• Univ. of Electronic Science & Technology, Chengdu, China

  Hong Cheng

Dr. Hong Cheng is Professor in the School of Automation Engineering, and Deputy Executive Director of the Center for Robotics at the University of Electronic Science and Technology of China. His other publications include the Springer book Autonomous Intelligent Vehicles.

Policies and ethics