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Robust Recognition via Information Theoretic Learning [electronic resource] / by Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang.

By: He, Ran [author.].
Contributor(s): Hu, Baogang [author.] | Yuan, Xiaotong [author.] | Wang, Liang [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XI, 110 p. 29 illus., 25 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319074160.Subject(s): Computer science | Computer graphics | Image processing | Computer Science | Computer Imaging, Vision, Pattern Recognition and Graphics | Image Processing and Computer VisionAdditional physical formats: Printed edition:: No titleDDC classification: 006.6 Online resources: Click here to access online
Contents:
Introduction -- M-estimators and Half-quadratic Minimization -- Information Measures -- Correntropy and Linear Representation -- �1 Regularized Correntropy -- Correntropy with Nonnegative Constraint.
In: Springer eBooksSummary: 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.
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Introduction -- M-estimators and Half-quadratic Minimization -- Information Measures -- Correntropy and Linear Representation -- �1 Regularized Correntropy -- Correntropy with Nonnegative Constraint.

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.

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