  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.
