Data & IT
Pocket
Robust Recognition via Information Theoretic Learning
Ran He • Baogang Hu • Xiaotong Yuan • Liang Wang
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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.
- Illustratör: 12 schwarz-weiße Tabellen 4 schwarz-weiße und 24 farbige Abbildungen Bibliographie
- Format: Pocket/Paperback
- ISBN: 9783319074153
- Språk: Engelska
- Antal sidor: 110
- Utgivningsdatum: 2014-09-09
- Förlag: Springer International Publishing AG