mlsanityData to 14 Oct 2026

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

arXiv 1805.07909 ↗First posted 21 May 2018cs.LG stat.MLcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 May 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 0; 2024: 3; 2025: 1; 2026: 1.

14 papers.

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  1. 2021MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Trackingcited by 9
  2. 2022Fast and explainable clustering based on sortingcited by 4
  3. 2021An Efficient One-Class SVM for Anomaly Detection in the Internet of Thingscited by 3
  4. 2021Git: Clustering Based on Graph of Intensity Topologycited by 3
  5. 2026SDiT: Semantic Region-Adaptive for Diffusion Transformerscited by 3
  6. 2022Clustering by Hill-Climbing: Consistency Resultscited by 2
  7. 2024Unsupervised Pre-Training for 3D Leaf Instance Segmentationcited by 2
  8. 2021Pre-Clustering Point Clouds of Crop Fields Using Scalable Methodscited by 1
  9. 2021An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Treecited by 1
  10. 2022How to scale hyperparameters for quickshift image segmentationcited by 1
  11. 2024TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimizationcited by 1
  12. 2024Beyond Attribution: Unified Concept-Level Explanationscited by 1
2 more
  1. 2022Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Setscited by 0
  2. 2025On the Burstiness of Faces in Setcited by 0