Robust Recognition via Information Theoretic Learning / by Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang.

Format:

Book

Author/Creator:

He, Ran, author.

Hu, Baogang, author.

Yuan, Xiaotong, author.

Wang, Liang, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

SpringerBriefs in computer science 2191-5768

SpringerBriefs in Computer Science, 2191-5768

Language:

English

Subjects (All):

Optical data processing.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Image Processing and Computer Vision.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Image Processing and Computer Vision.

Physical Description:

1 online resource (XI, 110 pages) : 29 illustrations, 25 illustrations in color.

Edition:

First edition 2014.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2014.

System Details:

text file PDF

Summary:

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.

Contents:

Introduction

M-estimators and Half-quadratic Minimization

Information Measures

Correntropy and Linear Representation

ℓ1 Regularized Correntropy

Correntropy with Nonnegative Constraint.

Other Format:

Printed edition:

ISBN:

978-3-319-07416-0

9783319074160

Access Restriction:

Restricted for use by site license.