Scalable Subspace Clustering with Application to Motion Segmentation

2015 ◽  
pp. 307-326
Keyword(s):  
Motion Segmentation ◽  
Subspace Clustering

scholarly journals Multibody Nonrigid Structure from Motion Segmentation Based on Sparse Subspace Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenqing Huang ◽  
Qingfeng Hu ◽  
Yaming Wang ◽  
Mingfeng Jiang
Keyword(s):  
Hierarchical Clustering ◽  
Structure From Motion ◽  
Motion Segmentation ◽  
Subspace Clustering ◽  
Weight Matrix ◽  
Strong Connection ◽  
Data Points ◽  
Entire Sequence ◽  
Sparse Subspace Clustering

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.

scholarly journals Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction

2019 ◽  
Vol 9 (8) ◽  
pp. 1559
Author(s):  
Weinan Du ◽  
Jinghua Li ◽  
Fei Wu ◽  
Yanfeng Sun ◽  
Yongli Hu
Keyword(s):  
State Of The Art ◽  
Motion Segmentation ◽  
Rigid Motion ◽  
Subspace Clustering ◽  
Sequential Data ◽  
Dynamic Scene ◽  
Normalized Mutual Information ◽  
3D Data ◽  
Segmentation Approach ◽  
Representation Method

As a fundamental and challenging problem, non-rigid structure-from-motion (NRSfM) has attracted a large amount of research interest. It is worth mentioning that NRSfM has been applied to dynamic scene understanding and motion segmentation. Especially, a motion segmentation approach combining NRSfM with the subspace representation has been proposed. However, the current subspace representation for non-rigid motions clustering do not take into account the inherent sequential property, which has been proved vital for sequential data clustering. Hence this paper proposes a novel framework to segment the complex and non-rigid motion via an ordered subspace representation method for the reconstructed 3D data, where the sequential property is properly formulated in the procedure of learning the affinity matrix for clustering with simultaneously recovering the 3D non-rigid motion by a monocular camera with 2D point tracks. Experiment results on three public sequential action datasets, BU-4DFE, MSR and UMPM, verify the benefits of method presented in this paper for classical complex non-rigid motion analysis and outperform state-of-the-art methods with lowest subspace clustering error (SCE) rates and highest normalized mutual information (NMI) in subspace clustering and motion segmentation fields.

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
Keyword(s):  
Motion Segmentation ◽  
Subspace Clustering ◽  
Human Motion ◽  
Sparse Subspace Clustering

Subspace Clustering Based on Differential Evolution

2012 ◽  
Vol 35 (10) ◽  
pp. 2116 ◽  
Author(s):  
Zhi-Sheng BI ◽  
Jia-Hai WANG ◽  
Jian YIN
Keyword(s):  
Differential Evolution ◽  
Subspace Clustering
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