Robust Subspace Estimation Using Low-Rank Optimization

Theory and Applications

  • Omar Oreifej
  • Mubarak Shah

Part of the The International Series in Video Computing book series (VICO, volume 12)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Omar Oreifej, Mubarak Shah
    Pages 1-7
  3. Omar Oreifej, Mubarak Shah
    Pages 9-19
  4. Omar Oreifej, Mubarak Shah
    Pages 21-36
  5. Omar Oreifej, Mubarak Shah
    Pages 55-67
  6. Omar Oreifej, Mubarak Shah
    Pages 69-93
  7. Omar Oreifej, Mubarak Shah
    Pages 95-99
  8. Omar Oreifej, Mubarak Shah
    Pages 101-108
  9. Back Matter
    Pages 109-114

About this book

Introduction

Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate  how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.

Keywords

Activity recognition complex event recognition computer vision image processing low-rank optimization machine learning motion decomposition motion estimation particle advection principal component analysis robust subspace estimation seeing through water sparse representation turbulence mitigation video denoising

Authors and affiliations

  • Omar Oreifej
    • 1
  • Mubarak Shah
    • 2
  1. 1.University of California, BerkeleyBerkeleyUSA
  2. 2.University of Central FloridaOrlandoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-04184-1
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-04183-4
  • Online ISBN 978-3-319-04184-1
  • Series Print ISSN 1571-5205
  • About this book
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