名称：Efficient Mean-shift Clustering Using Gaussian KD-Tree
时间： 明天（9.27,周一） 下午3点
Mean shift 是一种功能强大的数据聚类算法，但该算法的主要缺点是计算量大。本文提出了一种基于Gaussian KD-tree 数据特征空间自适应采样的方法，同时结合GPU加速处理，有效地解决了mean-shift 聚类的速度问题。 该算法不仅速度快，同时能获得与标准Mean shift 聚类相似的结果。
Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD-tree in a high-dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clu-stering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time-varying sequences segmentation.