Téléchargez le livre : Robust Recognition via Information Theoretic Learning

Robust Recognition via Information Theoretic Learning

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Éditeur :

Springer


Collection :

SpringerBriefs in Computer Science

Paru le : 2014-08-28

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Description

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.
Pages
110 pages
Collection
SpringerBriefs in Computer Science
Parution
2014-08-28
Marque
Springer
EAN papier
9783319074153
EAN EPUB
9783319074160
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
11
Taille du fichier
1751 Ko
Prix
52,74 €

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