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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: He, Ran (Autor), Hu, Baogang (Autor), Yuan, Xiaotong (Autor), Wang, Liang (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2014.
Edición:1st ed. 2014.
Colección:SpringerBriefs in Computer Science,
Temas:
Image processing-Digital techniques.
Computer vision.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer Vision.
Acceso en línea:Texto Completo

MARC

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300 |a XI, 110 p. 29 illus., 25 illus. in color.  |b online resource. 
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505 0 |a Introduction -- M-estimators and Half-quadratic Minimization -- Information Measures -- Correntropy and Linear Representation -- ℓ1 Regularized Correntropy -- Correntropy with Nonnegative Constraint. 
520 |a 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. 
650 0 |a Image processing-Digital techniques. 
650 0 |a Computer vision. 
650 1 4 |a Computer Imaging, Vision, Pattern Recognition and Graphics. 
650 2 4 |a Computer Vision. 
700 1 |a Hu, Baogang.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Yuan, Xiaotong.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Wang, Liang.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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