| 书目名称 | Robust Recognition via Information Theoretic Learning | | 编辑 | Ran He,Baogang Hu,Liang Wang | | 视频video | | | 概述 | Includes supplementary material: | | 丛书名称 | SpringerBriefs in Computer Science | | 图书封面 |  | | 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 | | 关键词 | 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 |
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发表于 2025-3-21 16:08:21
