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.
- Book Title Robust Recognition via Information Theoretic Learning
- Series Title SpringerBriefs in Computer Science
- Series Abbreviated Title SpringerBriefs Computer Sci.
- 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 Computer Science (R0)
- Softcover ISBN 978-3-319-07415-3
- eBook ISBN 978-3-319-07416-0
- 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
Computer Imaging, Vision, Pattern Recognition and Graphics
Image Processing and Computer Vision
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