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

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

21 May 2018
Heinrich Jiang
J. Jang
Samory Kpotufe
    VLMOOD
ArXiv (abs)PDFHTML

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

10 / 10 papers shown
Title
TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization
TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization
Haowen Ma
Zhiguo Long
Hua Meng
48
0
0
19 Aug 2024
Bagged $k$-Distance for Mode-Based Clustering Using the Probability of
  Localized Level Sets
Bagged kkk-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets
H. Hang
58
0
0
18 Oct 2022
Clustering by Hill-Climbing: Consistency Results
Clustering by Hill-Climbing: Consistency Results
E. Arias-Castro
Wanli Qiao
64
0
0
18 Feb 2022
Fast and explainable clustering based on sorting
Fast and explainable clustering based on sorting
Xinye Chen
S. Güttel
46
11
0
03 Feb 2022
How to scale hyperparameters for quickshift image segmentation
How to scale hyperparameters for quickshift image segmentation
Damien Garreau
47
1
0
23 Jan 2022
Git: Clustering Based on Graph of Intensity Topology
Git: Clustering Based on Graph of Intensity Topology
Zhangyang Gao
Haitao Lin
Cheng Tan
Lirong Wu
Stan. Z Li
69
7
0
04 Oct 2021
Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods
Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods
H. J. Nelson
Nikolaos Papanikolopoulos
18
7
0
22 Jul 2021
An Efficient One-Class SVM for Anomaly Detection in the Internet of
  Things
An Efficient One-Class SVM for Anomaly Detection in the Internet of Things
Kun Yang
Samory Kpotufe
Nick Feamster
39
37
0
22 Apr 2021
MeanShift++: Extremely Fast Mode-Seeking With Applications to
  Segmentation and Object Tracking
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking
J. Jang
Heinrich Jiang
32
19
0
01 Apr 2021
Learning Physical Graph Representations from Visual Scenes
Learning Physical Graph Representations from Visual Scenes
Daniel M. Bear
Chaofei Fan
Damian Mrowca
Yunzhu Li
S. Alter
...
Jeremy Schwartz
Li Fei-Fei
Jiajun Wu
J. Tenenbaum
Daniel L. K. Yamins
SSLGNNSSegAI4CE
98
79
0
22 Jun 2020
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