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...
| Published in: | Springer eBooks |
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| Main Authors: | , , , |
| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
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| Series: | SpringerBriefs in Computer Science,
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1007/978-3-319-07416-0 Перейти в каталог НБ ТГУ |
| Summary: | 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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| Physical Description: | XI, 110 p. 29 illus., 25 illus. in color. online resource. |
| ISBN: | 9783319074160 |
| ISSN: | 2191-5768 |