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portada Robust Recognition via Information Theoretic Learning (Springerbriefs in Computer Science)
Type
Physical Book
Publisher
Author
Year
2014
Language
English
Pages
124
Format
Paperback
ISBN13
9783319074153
Edition No.
2014
Categories

Robust Recognition via Information Theoretic Learning (Springerbriefs in Computer Science)

Ran He; Baogang Hu; Xiaotong Yuan (Author) · Springer · Paperback

Robust Recognition via Information Theoretic Learning (Springerbriefs in Computer Science) - Ran He; Baogang Hu; Xiaotong Yuan

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Synopsis "Robust Recognition via Information Theoretic Learning (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.

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