Multi-View Subspace Clustering for Face Images
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 f...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/40524 |
| _version_ | 1848755894433611776 |
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| author | Zhang, X. Phung, D. Venkatesh, S. Pham, DucSon Liu, Wan-Quan |
| author_facet | Zhang, X. Phung, D. Venkatesh, S. Pham, DucSon Liu, Wan-Quan |
| author_sort | Zhang, X. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:03:33Z |
| format | Conference Paper |
| id | curtin-20.500.11937-40524 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:03:33Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-405242017-09-13T15:41:23Z Multi-View Subspace Clustering for Face Images Zhang, X. Phung, D. Venkatesh, S. Pham, DucSon Liu, Wan-Quan 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. 2016 Conference Paper http://hdl.handle.net/20.500.11937/40524 10.1109/DICTA.2015.7371289 restricted |
| spellingShingle | Zhang, X. Phung, D. Venkatesh, S. Pham, DucSon Liu, Wan-Quan Multi-View Subspace Clustering for Face Images |
| title | Multi-View Subspace Clustering for Face Images |
| title_full | Multi-View Subspace Clustering for Face Images |
| title_fullStr | Multi-View Subspace Clustering for Face Images |
| title_full_unstemmed | Multi-View Subspace Clustering for Face Images |
| title_short | Multi-View Subspace Clustering for Face Images |
| title_sort | multi-view subspace clustering for face images |
| url | http://hdl.handle.net/20.500.11937/40524 |