Robust Recognition via Information Theoretic Learning / / by Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
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| Autore: |
He Ran
|
| Titolo: |
Robust Recognition via Information Theoretic Learning / / by Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
|
| Pubblicazione: | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Edizione: | 1st ed. 2014. |
| Descrizione fisica: | 1 online resource (120 p.) |
| Disciplina: | 006.3 |
| 006.37 | |
| Soggetto topico: | Optical data processing |
| Computer Imaging, Vision, Pattern Recognition and Graphics | |
| Image Processing and Computer Vision | |
| Persona (resp. second.): | HuBaogang |
| YuanXiaotong | |
| WangLiang | |
| Note generali: | Description based upon print version of record. |
| Nota di bibliografia: | Includes bibliographical references. |
| Nota di contenuto: | Introduction -- M-estimators and Half-quadratic Minimization -- Information Measures -- Correntropy and Linear Representation -- ℓ1 Regularized Correntropy -- Correntropy with Nonnegative Constraint. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Robust Recognition via Information Theoretic Learning |
| ISBN: | 3-319-07416-4 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910298969003321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
SpringerBriefs in Computer Science, . 2191-5768