High-Dimensional Entropy Estimation for Finite Accuracy Data: R-NN Entropy Estimator
High-Dimensional Entropy Estimation for Finite Accuracy Data: R-NN Entropy Estimator
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有限精度数据的高维熵估计：R-NN 熵估计器
DOI：
10.1007/978-3-540-73273-0_47
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
J. Kybic
中科院分区：
文献类型：
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作者：
J. Kybic
我们解决了高维有限精度数据的熵估计问题。我们的主要应用是评估高阶互信息图像相似性标准的多模态图像配准。我们的方法的基础是基于第k个最近邻（NN）距离的估计，修改，使只有距离大于一些constantRare评估。这种修改需要在使用二次规划的预处理步骤中数值地发现的校正。我们实验比较我们的新方法与k-NN和直方图估计的合成数据，以及图像相似性的互信息的评价。
We address the problem of entropy estimation for high-dimensional finite-accuracy data. Our main application is evaluating high-order mutual information image similarity criteria for multimodal image registration. The basis of our method is an estimator based onk-th nearest neighbor (NN) distances, modified so that only distances greater than some constantRare evaluated. This modification requires a correction which is found numerically in a preprocessing step using quadratic programming. We compare experimentally our new method withk-NN and histogram estimators on synthetic data as well as for evaluation of mutual information for image similarity.