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Multi-view subspace clustering for face images
conference contribution
posted on 2015-11-25, 00:00 authored by Xin Zhang, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh, D S Pham, W Liu© 2015 IEEE. In many real-world computer vision applications, such as multi-camera surveillance, the objects of interest are captured by visual sensors concurrently, resulting in multi-view data. These views usually provide complementary information to each other. One recent and powerful computer vision method for clustering is sparse subspace clustering (SSC); however, it was not designed for multi-view data, which break down its linear separability assumption. To integrate complementary information between views, multi-view clustering algorithms are required to improve the clustering performance. In this paper, we propose a novel multi-view subspace clustering by searching for an unified latent structure as a global affinity matrix in subspace clustering. Due to the integration of affinity matrices for each view, this global affinity matrix can best represent the relationship between clusters. This could help us achieve better performance on face clustering. 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 alternatives based on state-of-The-Arts on challenging multi-view face datasets.
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International Conference on Digital Image Computing: Techniques and Applications (2015 : Adelaide, S. Aust.)Pagination
1 - 8Publisher
IEEELocation
Adelaide, S. Aust.Place of publication
Piscataway, N. J.Publisher DOI
Start date
2015-11-23End date
2015-11-25ISBN-13
9781467367950Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
DICTA 2015: Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and ApplicationsUsage metrics
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