Online Motion Segmentation Based on Sparse Subspace Clustering

2015 ◽  
Vol 12 (4) ◽  
pp. 1293-1300 ◽  
Author(s):  
Jianting Wang
Keyword(s):  
Motion Segmentation ◽  
Subspace Clustering ◽  
Sparse 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.

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

Block Diagonal Sparse Subspace Clustering

2021 ◽  
Author(s):  
Lili Fan ◽  
Guifu Lu ◽  
Yong Wang ◽  
Tao Liu
Keyword(s):  
Subspace Clustering ◽  
Sparse Subspace Clustering ◽  
Block Diagonal

scholarly journals Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels

TecnoLógicas ◽  
2019 ◽  
Vol 22 (46) ◽  
pp. 1-14 ◽  
Author(s):  
Jorge Luis Bacca ◽  
Henry Arguello
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Image Clustering ◽  
Spectral Signature ◽  
Spectral Image ◽  
Sparse Subspace Clustering

Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces.  Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

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