Effective measurement selection in truncated kernel density estimator: Voronoi mean shift algorithm for truncated kernels
Effective measurement selection in truncated kernel density estimator: Voronoi mean shift algorithm for truncated kernels
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DOI：
10.1145/1968613.1968683
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发表时间：
2011-02
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影响因子：
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通讯作者：
J. Yoon;Hyoung-joo Lee;Hyoungshick Kim
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文献类型：
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作者：
J. Yoon;Hyoung-joo Lee;Hyoungshick Kim
采用门控/截断技术来选择相对重要的测量值而不是所有的测量值来加快均值移位算法，均值移位算法是计算机视觉领域中著名的聚类算法之一。传统的均值移位算法对测量值的选择比较敏感，因为测量值被固定大小的高斯窗口截断。特别是当选择一个小的门控窗口时，它不能正确地聚类远离主要聚类的数据点，从而产生不需要的小聚类。我们提出了一种基于给定数据集的称为Voronoi图的几何结构的截断均值移位算法的鲁棒门控技术。与传统的门控/截断技术不同，我们提出的截断技术可以提供可变大小的非线性截断窗口，通过使用Voronoi图来有效地识别聚类中的离群点。我们还通过将其应用于合成和真实图像数据集来证明该技术的可行性。实验结果表明，与传统截断技术相比，该截断技术具有更强的鲁棒性。该算法可以有效地用于去除背景噪声的图像去噪。
The Gating/Truncation technique is adapted to choose relatively significant measurements rather than all measurements to speed up mean shift algorithm which is one of the well-known clustering algorithms in the field of computer vision. The conventional mean shift algorithm can be sensitive to selecting measurements since the measurements are truncated with a Gaussian window of a fixed size. In particular when a small gating window is selected, it cannot properly cluster data points located far from major clusters and thus it generates unwanted, small clusters. We present a robust gating technique for truncated mean shift algorithm based on a geometric structure called Voronoi diagram of a given data set. Unlike conventional gating/truncation techniques our proposed truncation technique can provide nonlinear truncation windows with variable sizes constructed by using the Voronoi diagram to effectively identify outlier points in clusters. We also demonstrate the feasibility of this technique by applying it on synthetic and real-world image data sets. The experimental results show that the proposed truncation technique provides a more robust clustering result compared to the conventional truncation techniques. The proposed algorithm can be effectively applied to denoising of images by removing background noise.