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  • Low-rank double dictionary learning from corrupted data for robust image classification

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    Author(s)
    Rong, Yi
    Xiong, Shengwu
    Gao, Yongsheng
    Griffith University Author(s)
    Gao, Yongsheng
    Year published
    2017
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    Abstract
    In this paper, we propose a novel low-rank double dictionary learning (LRD2L) method for robust image classification tasks, in which the training and testing samples are both corrupted. Unlike traditional dictionary learning methods, LRD2L simultaneously learns three components from corrupted training data: 1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative class-specific features of each class, 2) a low-rank class-shared dictionary which models the common patterns shared in the data of different classes, and 3) a sparse error term to model the noise in data. Through low-rank ...
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    In this paper, we propose a novel low-rank double dictionary learning (LRD2L) method for robust image classification tasks, in which the training and testing samples are both corrupted. Unlike traditional dictionary learning methods, LRD2L simultaneously learns three components from corrupted training data: 1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative class-specific features of each class, 2) a low-rank class-shared dictionary which models the common patterns shared in the data of different classes, and 3) a sparse error term to model the noise in data. Through low-rank class-shared dictionary and noise term, the proposed method can effectively separate the corruptions and noise in training samples from creating low-rank class-specific sub-dictionaries, which are employed for correctly reconstructing and classifying testing images. Comparative experiments are conducted on three public available databases. Experimental results are encouraging, demonstrating the effectiveness of the proposed method and its superiority in performance over the state-of-the-art dictionary learning methods.
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    Journal Title
    Pattern Recognition
    Volume
    72
    DOI
    https://doi.org/10.1016/j.patcog.2017.06.038
    Copyright Statement
    © 2017 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/351544
    Collection
    • Journal articles

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