On the Consistency of Quick Shift
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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.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Oct 2017.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 1; 2025: 1.
7 papers.
- 2021MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Trackingcited by 9
- 2022Clustering by Hill-Climbing: Consistency Resultscited by 2
- 2021An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Treecited by 1
- 2022How to scale hyperparameters for quickshift image segmentationcited by 1
- 2024TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimizationcited by 1
- 2022Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Setscited by 0
- 2025Almost Linear Time Consistent Mode Estimation and Quick Shift Clusteringcited by 0
- 2025Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering11 Mar 2025
- 2024TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization19 Aug 2024
- 2022Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets18 Oct 2022
- 2022Clustering by Hill-Climbing: Consistency Results18 Feb 2022
- 2022How to scale hyperparameters for quickshift image segmentation23 Jan 2022
- 2021An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Tree19 Nov 2021
- 2021MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking1 Apr 2021