Description
Robust Recognition via Information Theoretic Learning, 2014
SpringerBriefs in Computer Science Series
Authors: He Ran, Hu Baogang, Yuan Xiaotong, Wang Liang
Language: EnglishSubjects for Robust Recognition via Information Theoretic Learning:
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In Print (Delivery period: 15 days).
Add to cart the book of He Ran, Hu Baogang, Yuan Xiaotong, Wang Liang110 p. · 15.5x23.5 cm · Paperback
Description
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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.
Includes supplementary material: sn.pub/extras