Visual object clustering via mixed-norm regularization

Zhang, Xin, Pham, Duc-Son, Phung, Dinh, Liu, Wanquan, Saha, Budhaditya and Venkatesh, Svetha 2015, Visual object clustering via mixed-norm regularization, in WACV 2015: Proceedings of the 15th IEEE Winter Conference on Applications of Computer Vision, IEEE, Piscataway, N.J., pp. 1030-1037, doi: 10.1109/WACV.2015.142.

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Title Visual object clustering via mixed-norm regularization
Author(s) Zhang, Xin
Pham, Duc-Son
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Liu, Wanquan
Saha, BudhadityaORCID iD for Saha, Budhaditya orcid.org/0000-0001-8011-6801
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name IEEE Winter Conference on Applications of Computer Vision (15th : 2015 : Waikoloa, Hawaii)
Conference location Waikoloa, Hawaii
Conference dates 05-09 Jan. 2015
Title of proceedings WACV 2015: Proceedings of the 15th IEEE Winter Conference on Applications of Computer Vision
Publication date 2015
Start page 1030
End page 1037
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.
ISBN 9781479966820
Language eng
DOI 10.1109/WACV.2015.142
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076874

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