论文标题
具有三角内核轮廓的广义平均移位
Generalized mean shift with triangular kernel profile
论文作者
论文摘要
平均移位算法是一种流行的方式，可以找到采用特定基于内核的形状的某些概率密度函数的模式，用于聚类或视觉跟踪。自引入以来，它经历了几种实际的改进和概括，以及深入的理论分析，主要集中于其收敛性。尽管结果令人鼓舞，但这个问题尚未得到明确的总答案。在本文中，我们专注于特定类别的内核，特别适合于激发这项工作的分布聚类应用程序。我们表明，可以得出适应它们的新型平均变化变体，并在有限的迭代次数之后被证明会收敛。为了在平均偏移理论的一般情况下，将这种新的方法置于该领域现有结果的合成暴露。
The mean shift algorithm is a popular way to find modes of some probability density functions taking a specific kernel-based shape, used for clustering or visual tracking. Since its introduction, it underwent several practical improvements and generalizations, as well as deep theoretical analysis mainly focused on its convergence properties. In spite of encouraging results, this question has not received a clear general answer yet. In this paper we focus on a specific class of kernels, adapted in particular to the distributions clustering applications which motivated this work. We show that a novel Mean Shift variant adapted to them can be derived, and proved to converge after a finite number of iterations. In order to situate this new class of methods in the general picture of the Mean Shift theory, we alo give a synthetic exposure of existing results of this field.
