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    Multi-View Subspace Clustering for Face Images

    Access Status
    Fulltext not available
    Authors
    Zhang, X.
    Phung, D.
    Venkatesh, S.
    Pham, DucSon
    Liu, Wan-Quan
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Abstract

    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.

    Citation
    Zhang, X. and Phung, D. and Venkatesh, S. and Pham, D. and Liu, W. 2016. Multi-View Subspace Clustering for Face Images, in Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Nov 23-25 2015. Adelaide, SA: IEEE.
    Source Title
    2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
    DOI
    10.1109/DICTA.2015.7371289
    ISBN
    9781467367950
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/40524
    Collection
    • Curtin Research Publications

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