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.
DICTA 2015: Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications
Place of publication
Piscataway, N. J.
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.
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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