Robust Recognition via Information Theoretic Learning
Produktnummer:
18017fa183b8d34e7cb80bc4b158d40380
| Autor: | He, Ran Hu, Baogang Wang, Liang Yuan, Xiaotong |
|---|---|
| Themengebiete: | Face recognition information theoretic learning large scale robust estimation sparse representation |
| Veröffentlichungsdatum: | 09.09.2014 |
| EAN: | 9783319074153 |
| Sprache: | Englisch |
| Seitenzahl: | 110 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | Springer International Publishing |
Produktinformationen "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 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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