PFS: ParticleFilterBased Superpixel Segmentation
Abstract
:1. Introduction
 We propose a novel superpixel segmentation method based on the particle filter, which extends the original tracking problem to region clustering. Multiple particles are used to approximate superpixel centers, which could highly dispersedly be located in feature space to generate a much lower intracluster distance.
 Moving and perturbing processes are introduced to guide the propagation step of those particles, which reformulates the particle filter to fit our region cluster problem.
 The segmentation performance of PFS outperforms seven stateoftheart methods on a quantitative benchmark.
2. Related Work
3. Particle Filtering
4. Superpixel Segmentation via Particle Filtering
4.1. Problem Formulation
4.2. Initial Particles’ Selection
4.3. Propagation
4.3.1. Movement
4.3.2. Perturbation
4.4. Evaluation and Estimation
Algorithm 1 Algorithm for PFS. 
Require:

4.5. Selection
5. Experiment
5.1. Performance Evaluation
5.2. Visual Comparison
6. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
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No. Particles  1  3  5  7  9 

K = 100  $0.7262$  $0.7281$  $0.7290$  $0.7285$  $0.7285$ 
K = 500  $0.9144$  $0.9209$  $0.9298$  $0.9288$  $0.9289$ 
Method  Ours  LSC  SLIC  SNIC  NC  TP  RPS  FH 

Time (s)  $2.45$  $0.35$  $0.33$  $0.11$  $44.73$  $6.88$  $13.6$  $0.689$ 
Code  matlab + C  C++  C++  C++  matlab  C++  matlab  C++ 
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Xu, L.; Luo, B.; Pei, Z.; Qin, K. PFS: ParticleFilterBased Superpixel Segmentation. Symmetry 2018, 10, 143. https://doi.org/10.3390/sym10050143
Xu L, Luo B, Pei Z, Qin K. PFS: ParticleFilterBased Superpixel Segmentation. Symmetry. 2018; 10(5):143. https://doi.org/10.3390/sym10050143
Chicago/Turabian StyleXu, Li, Bing Luo, Zheng Pei, and Keyun Qin. 2018. "PFS: ParticleFilterBased Superpixel Segmentation" Symmetry 10, no. 5: 143. https://doi.org/10.3390/sym10050143