Robust Recognition via Information Theoretic Learning - Xiaotong Yuan,Ran He,Liang Wang,Baogang Hu
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Toimitus 12-18 arkipäivässä
30 päivän palautusoikeus
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 conc ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Xiaotong Yuan, Ran He, Liang Wang, Baogang Hu |
|---|---|
| Julkaisija | Springer Nature Switzerland |
| Series | SpringerBriefs in Computer Science |
| Julkaisuvuosi | 2014 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9783319074153 |