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Robust Recognition via Information Theoretic Learning, 2014 SpringerBriefs in Computer Science Series

Langue : Anglais

Auteurs :

Couverture de l’ouvrage Robust Recognition via Information Theoretic Learning
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.
Introduction.- M-estimators and Half-quadratic Minimization.- Information Measures.- Correntropy and Linear Representation.- ℓ1 Regularized Correntropy.- Correntropy with Nonnegative Constraint.

Date de parution :

Ouvrage de 110 p.

15.5x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).

Prix indicatif 58,00 €

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