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Quickshift++: Provably Good Initializations for Sample-Based Mean Shift

2020-03-11 | |  75 |   41 |   0

Abstract

We provide initial seedings to the Quick Shift clu tering 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 fo this modification. We then show strong clustering performance on real datasets as well as promising applications to image segmentation.

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