Multi-view subspace clustering for face images

Zhang, Xin, Phung, Dinh, Venkatesh, Svetha, Pham, Duc Son and Liu, Wanquan 2015, Multi-view subspace clustering for face images, in DICTA 2015: Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications, IEEE, Piscataway, N. J., pp. 1-8, doi: 10.1109/DICTA.2015.7371289.

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Title Multi-view subspace clustering for face images
Author(s) Zhang, Xin
Phung, DinhORCID iD for Phung, Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Pham, Duc Son
Liu, Wanquan
Conference name International Conference on Digital Image Computing: Techniques and Applications (2015 : Adelaide, S. Aust.)
Conference location Adelaide, S. Aust.
Conference dates 23-25 Nov. 2015
Title of proceedings DICTA 2015: Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications
Publication date 2015
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N. J.
Summary 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.
ISBN 9781467367950
Language eng
DOI 10.1109/DICTA.2015.7371289
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
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