Human Motion Segmentation via Robust Kernel Sparse Subspace Clustering

2018 ◽  
Vol 27 (1) ◽  
pp. 135-150 ◽  
Author(s):  
Guiyu Xia ◽  
Huaijiang Sun ◽  
Lei Feng ◽  
Guoqing Zhang ◽  
Yazhou Liu
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenqing Huang ◽  
Qingfeng Hu ◽  
Yaming Wang ◽  
Mingfeng Jiang

Sparse subspace clustering (SSC) is one of the latest methods of dividing data points into subspace joints, which has a strong theoretical guarantee. However, affine matrix learning is not very effective for segmenting multibody nonrigid structure from motion. To improve the segmentation performance and efficiency of the SSC algorithm in segmenting multiple nonrigid motions, we propose an algorithm that deploys the hierarchical clustering to discover the inner connection of data and represents the entire sequence using some of trajectories (in this paper, we refer to these trajectories as the set of anchor trajectories). Only the corresponding positions of the anchor trajectories have nonzero weights. Furthermore, in order to improve the affinity coefficient and strong connection between trajectories in the same subspace, we optimise the weight matrix by integrating the multilayer graphs and good neighbors. The experiments prove that our methods are effective.


2021 ◽  
Vol 65 (1) ◽  
Author(s):  
Hongbo Gao ◽  
Fang Guo ◽  
Juping Zhu ◽  
Zhen Kan ◽  
Xinyu Zhang

2021 ◽  
Author(s):  
Lili Fan ◽  
Guifu Lu ◽  
Yong Wang ◽  
Tao Liu

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