Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947464 | Neurocomputing | 2017 | 9 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Shiwei Yu, Ting-Zhu Huang,