Robust Recognition via Information Theoretic Learning
Springer International Publishing (Verlag)
978-3-319-07415-3 (ISBN)
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.
Introduction.- M-estimators and Half-quadratic Minimization.- Information Measures.- Correntropy and Linear Representation.- 1 Regularized Correntropy.- Correntropy with Nonnegative Constraint.
Erscheint lt. Verlag | 9.9.2014 |
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Reihe/Serie | SpringerBriefs in Computer Science |
Zusatzinfo | XI, 110 p. 29 illus., 25 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 201 g |
Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Schlagworte | face recognition • Information theoretic learning • large scale • robust estimation • Sparse Representation |
ISBN-10 | 3-319-07415-6 / 3319074156 |
ISBN-13 | 978-3-319-07415-3 / 9783319074153 |
Zustand | Neuware |
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