| 書目名稱 | Robust Recognition via Information Theoretic Learning |
| 編輯 | Ran He,Baogang Hu,Liang Wang |
| 視頻video | http://file.papertrans.cn/832/831353/831353.mp4 |
| 概述 | Includes supplementary material: |
| 叢書名稱 | SpringerBriefs in Computer Science |
| 圖書封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2014 |
| 關鍵詞 | Face recognition; information theoretic learning; large scale; robust estimation; sparse representation |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-07416-0 |
| isbn_softcover | 978-3-319-07415-3 |
| isbn_ebook | 978-3-319-07416-0Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s) 2014 |
|Archiver|手機版|小黑屋|
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