We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then show strong clustering performance on real datasets as well as promising applications to image segmentation.

翻译：我们向快速移位群集算法提供初始种子,该算法近似于当地数据密度高的区域。 此类种子比快速移位所发现的单吨模式更稳定、更显眼的群集核心。 我们为这一修改建立了统计一致性保障。 然后,我们在真实的数据集上展示了强大的群集性能,并展示了有希望的图像分割应用。