Robust Recognition via Information Theoretic Learning

  • Ran He
  • Baogang Hu
  • Xiaotong Yuan
  • Liang Wang

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
    Pages 1-2
  3. Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
    Pages 3-11
  4. Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
    Pages 13-44
  5. Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
    Pages 45-60
  6. Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
    Pages 61-83
  7. Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
    Pages 85-102
  8. Back Matter
    Pages 103-110

About this book

Introduction

This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.

The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.

Keywords

Face recognition information theoretic learning large scale robust estimation sparse representation

Authors and affiliations

  • Ran He
    • 1
  • Baogang Hu
    • 2
  • Xiaotong Yuan
    • 3
  • Liang Wang
    • 4
  1. 1.National Laboratory of Pattern RecognitionInstitute of Automation Chinese Academy of SciencesBeijingChina
  2. 2.National Laboratory of Pattern RecognitionInstitute of Automation Chinese Academy of SciencesBeijingChina
  3. 3.School of Information and ControlNanjing University of Information Science and TechnologyNanjingChina
  4. 4.Institute of Automaton Chinese Academy of SciencesBeijingChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-07416-0
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-07415-3
  • Online ISBN 978-3-319-07416-0
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • About this book
Industry Sectors
Automotive
Biotechnology
Electronics
Telecommunications
Aerospace