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Sparse Representation, Modeling and Learning in Visual Recognition

Theory, Algorithms and Applications

  • Book
  • © 2015

Overview

Authors:
  1. Hong Cheng

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

    View author publications

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  • Describes the latest research trends in compressed sensing, covering sparse representation, modeling and learning
  • Examines sensing applications in visual recognition, including sparsity induced similarity, and sparse coding-based classifying frameworks
  • Discusses in detail the theory and algorithms of compressed sensing
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Introduction and Fundamentals

    1. Front Matter

      Pages 1-1
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    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
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    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
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    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
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    2. Beyond Sparsity

      • Hong Cheng
      Pages 215-235

  6. Back Matter

    Pages 237-257
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Keywords

About this book

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.

Authors and Affiliations

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

    Hong Cheng

About the author

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.

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