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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.
| Autorius: | Ran He, Liang Wang, Xiaotong Yuan, Baogang Hu, |
| Serija: | SpringerBriefs in Computer Science |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2014 |
| Knygos puslapių skaičius: | 124 |
| ISBN-10: | 3319074156 |
| ISBN-13: | 9783319074153 |
| Formatas: | 235 x 155 x 8 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Robust Recognition via Information Theoretic Learning“
