# On the Consistency of Quick Shift

arXiv: https://arxiv.org/abs/1710.10646 · HTML: https://mlsanity.com/paper/1710.10646/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking](https://mlsanity.com/paper/2104.00303/) (2021)
- [Clustering by Hill-Climbing: Consistency Results](https://mlsanity.com/paper/2202.09023/) (2022)
- [An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Tree](https://mlsanity.com/paper/2111.10298/) (2021)
- [How to scale hyperparameters for quickshift image segmentation](https://mlsanity.com/paper/2201.09286/) (2022)
- [TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization](https://mlsanity.com/paper/2408.10084/) (2024)
- [Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets](https://mlsanity.com/paper/2210.09786/) (2022)
- [Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering](https://mlsanity.com/paper/2503.07995/) (2025)

## Abstract (arXiv)

Quick Shift is a popular mode-seeking and clustering algorithm. We present finite sample statistical consistency guarantees for Quick Shift on mode and cluster recovery under mild distributional assumptions. We then apply our results to construct a consistent modal regression algorithm.