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A robust competitive clustering algorithm with applications in computer vision

机译:强大的竞争性聚类算法及其在计算机视觉中的应用

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This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers. The proposed robust competitive agglomeration (RCA) algorithm starts with a large number of clusters to reduce the sensitivity to initialization, and determines the actual number of clusters by a process of competitive agglomeration. Noise immunity is achieved by incorporating concepts from robust statistics into the algorithm. RCA assigns two different sets of weights for each data point: the first set of constrained weights represents degrees of sharing, and is used to create a competitive environment and to generate a fuzzy partition of the data set. The second set corresponds to robust weights, and is used to obtain robust estimates of the cluster prototypes. By choosing an appropriate distance measure in the objective function, RCA can be used to find an unknown number of clusters of various shapes in noisy data sets, as well as to fit an unknown number of parametric models simultaneously. Several examples, such as clustering/mixture decomposition, line/plane fitting, segmentation of range images, and estimation of motion parameters of multiple objects, are shown.
机译:本文解决了与常规分区聚类相关的三个主要问题,即对初始化的敏感性,确定簇数的难度以及对噪声和异常值的敏感性。所提出的鲁棒竞争聚集(RCA)算法从大量簇开始以降低初始化的敏感性,并通过竞争聚集过程确定簇的实际数量。通过将鲁棒统计中的概念纳入算法,可以实现抗扰性。 RCA为每个数据点分配两组不同的权重:第一组受约束的权重代表共享度,并用于创建竞争环境并生成数据集的模糊分区。第二组对应于鲁棒权重,用于获得聚类原型的鲁棒估计。通过在目标函数中选择合适的距离度量,RCA可用于在嘈杂的数据集中找到未知数量的各种形状的簇,并同时拟合未知数量的参数模型。显示了几个示例,例如聚类/混合分解,线/平面拟合,范围图像的分割以及多个对象的运动参数的估计。

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