Robust Recognition via Information Theoretic Learning - Couverture souple

He, Ran; Hu, Baogang; Yuan, Xiaotong; Wang, Liang

Couverture souple

ISBN 10 : 3319074156 ISBN 13 : 9783319074153

Editeur : Springer, 2014

Synopsis

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.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2014
• Langue: anglais
• ISBN 10: 3319074156
• ISBN 13: 9783319074153
• Reliure: Broché
• Nombre de pages: 124
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