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Symmetry 2018, 10(5), 143;

PFS: Particle-Filter-Based Superpixel Segmentation

1,2,* , 3
The Postdoctoral Station at Xihua University Based on Collaboration Innovation Center of Sichuan Automotive Key Parts, Xihua University, Chengdu 610039, China
The School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
The Center for Radio Administration Technology Development, Xihua University, Chengdu 610039, China
Author to whom correspondence should be addressed.
Received: 30 March 2018 / Revised: 19 April 2018 / Accepted: 26 April 2018 / Published: 4 May 2018
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In this paper, we propose a particle-filter-based superpixel (PFS) segmentation method that extends the original tracking problem as a region clustering problem. The basic idea is to approximate superpixel centers by multiple particles to obtain high intra-region similarity. Specifically, we firstly use a density cluster to initialize single-group particles and introduce the association rule for mining other initial candidate particles. In propagation, particles are transferred to neighboring local regions by a moving step aiming to update local candidate particles with a lower energy cost. We evaluate all particles on the basis of their cluster similarity and estimate the largest particles as the final superpixel centers. The proposed method can locate cluster centers in diverse feature space, which alleviates the risk of a local optimum and produces better segmentation performance. Experimental results on the Berkeley segmentation 500 dataset (BSD500) demonstrate that our method outperforms seven state-of-the-art methods. View Full-Text
Keywords: superpixel segmentation; particle filtering; density cluster; associate rule; intra-region similarity superpixel segmentation; particle filtering; density cluster; associate rule; intra-region similarity

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Xu, L.; Luo, B.; Pei, Z.; Qin, K. PFS: Particle-Filter-Based Superpixel Segmentation. Symmetry 2018, 10, 143.

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