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Visual object clustering via mixed-norm regularization
conference contribution
posted on 2015-01-01, 00:00 authored by Xin Zhang, D Pham, Quoc-Dinh Phung, W Liu, Budhaditya Saha, Svetha VenkateshSvetha VenkateshMany 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.
History
Event
IEEE Winter Conference on Applications of Computer Vision (15th : 2015 : Waikoloa, Hawaii)Pagination
1030 - 1037Publisher
IEEELocation
Waikoloa, HawaiiPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-01-05End date
2015-01-09ISBN-13
9781479966820Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
WACV 2015: Proceedings of the 15th IEEE Winter Conference on Applications of Computer VisionUsage metrics
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