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Exponential weighted entropy and exponential weighted mutual information

机译:指数加权熵和指数加权互信息

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摘要

In this paper, the exponential weighted entropy (EWE) and exponential weighted mutual information (EWMI) are proposed as the more generalized forms of Shannon entropy and mutual information (MI), respectively. They are position-related and causal systems that redefine the foundations of information theoretic metrics. As the special forms of the weighted entropy and the weighted mutual information, EWE and EWMI have been proved that they preserve nonnegativity and concavity properties similar to Shannon frameworks. They can be adopted as the information measures in spatial interaction modeling. Paralleling with the normalized mutual information (NMI), the normalized exponential weighted mutual information (NEWMI) is also investigated. Image registration experiments demonstrate that EWMI and NEWMI algorithms can achieve higher aligned accuracy than MI and NMI algorithms. 2017 Elsevier B.V. All rights reserved.
机译:在本文中,提出了指数加权熵(EWE)和指数加权互信息(EWMI)作为Shannon熵和互信息(MI)的更广义形式。它们是与位置相关的因果系统,重新定义了信息理论指标的基础。作为加权熵和加权互信息的特殊形式,已证明EWE和EWMI保留了类似于Shannon框架的非负性和凹性。它们可以用作空间交互建模中的信息度量。与标准化互信息(NMI)并行,还研究了标准化指数加权互信息(NEWMI)。图像配准实验表明,EWMI和NEWMI算法比MI和NMI算法可以实现更高的对齐精度。 2017 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2017年第2期|86-94|共9页
  • 作者

    Yu Shiwei; Huang Ting-Zhu;

  • 作者单位

    Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China;

    Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Entropy; Mutual information; Concavity; Image registration;

    机译:熵;相互信息;凹度;图像配准;

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