Robust Recognition via Information Theoretic Learning [electronic resource] /
He, Ran.author.
Hu, Baogang.author.
Yuan, Xiaotong.author.
Wang, Liang.author.
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.
Description based upon print version of record.
Includes bibliographical references.
Introduction -- M-estimators and Half-quadratic Minimization -- Information Measures -- Correntropy and Linear Representation -- ℓ1 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.
Optical data processing.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Image Processing and Computer Vision.