Robust Recognition via Information Theoretic Learning

2014
ISBN: 978-3-319-07415-3
Verlag: Springer International Publishing

Buch, Englisch, 110 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 201 g

Reihe: SpringerBriefs in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

He, Ran

Wang, Liang

Hu, Baogang

Yuan, Xiaotong

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Introduction.- M-estimators and Half-quadratic Minimization.- Information Measures.- Correntropy and Linear Representation.- l1 Regularized Correntropy.- Correntropy with Nonnegative Constraint.