Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (6 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Robust Recognition via Information Theoretic Learning
Authors: Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-07416-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2014
Softcover ISBN: 978-3-319-07415-3Published: 09 September 2014
eBook ISBN: 978-3-319-07416-0Published: 28 August 2014
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XI, 110
Number of Illustrations: 4 b/w illustrations, 25 illustrations in colour
Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Image Processing and Computer Vision