论文标题
对于标准化无法解释的方差（NUV）差异度量的分段常数近似值的近似最佳套件
Approximately Optimal Binning for the Piecewise Constant Approximation of the Normalized Unexplained Variance (nUV) Dissimilarity Measure
论文作者
论文摘要
最近通过音调映射（MTM）差异度量匹配的匹配度量可以在平滑的非线性畸变下匹配模板，并且具有完善的数学背景。 MTM通过夹住模板来操作，但是特定问题的理想套架是一个空旷的问题。通过指出众所周知的相互信息（MI）和MTM之间的重要类比，我们介绍了“归一化的无法解释的差异”一词（NUV），以强调其超出图像处理以外的相关性和适用性。然后，我们为NUV度量的最佳嵌入技术提供理论结果，并提出算法以找到近似解决方案。理论发现由数值实验支持。使用所提出的融合技术显示出具有统计学意义的AUC分数增长4-13％，从而使我们得出结论，所提出的固定技术有可能提高NUV量度在实际应用中的性能。
The recently introduced Matching by Tone Mapping (MTM) dissimilarity measure enables template matching under smooth non-linear distortions and also has a well-established mathematical background. MTM operates by binning the template, but the ideal binning for a particular problem is an open question. By pointing out an important analogy between the well known mutual information (MI) and MTM, we introduce the term "normalized unexplained variance" (nUV) for MTM to emphasize its relevance and applicability beyond image processing. Then, we provide theoretical results on the optimal binning technique for the nUV measure and propose algorithms to find approximate solutions. The theoretical findings are supported by numerical experiments. Using the proposed techniques for binning shows 4-13% increase in terms of AUC scores with statistical significance, enabling us to conclude that the proposed binning techniques have the potential to improve the performance of the nUV measure in real applications.
