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    Classification of corals in reflectance and fluorescence images using convolutional neural network representations

    Access Status
    Fulltext not available
    Authors
    Xu, L.
    Bennamoun, M.
    An, Senjian
    Sohel, F.
    Boussaid, F.
    Date
    2018
    Type
    Conference Paper
    
    Metadata
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    Citation
    Xu, L. and Bennamoun, M. and An, S. and Sohel, F. and Boussaid, F. 2018. Classification of corals in reflectance and fluorescence images using convolutional neural network representations, pp. 1493-1497.
    Source Title
    ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    DOI
    10.1109/ICASSP.2018.8462574
    ISBN
    9781538646588
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/71733
    Collection
    • Curtin Research Publications
    Abstract

    © 2018 IEEE. Coral species, with complex morphology and ambiguous boundaries, pose a great challenge for automated classification. CNN activations, which are extracted from fully connected layers of deep networks (FC features), have been successfully used as powerful universal representations in many visual tasks. In this paper, we investigate the transferability and combined performance of FC features and CONY features (extracted from convolutional layers) in the coral classification of two image modalities (reflectance and fluorescence), using a typical deep network (e.g. VGGNet). We exploit vector of locally aggregated descriptors (VLAD) encoding and principal component analysis (PCA) to compress dense CONY features into a compact representation. Experimental results demonstrate that encoded CONV3 features achieve superior performances on reflectance and fluorescence coral images, compared to FC features. The combination of these two features further improves the overall accuracy and achieves state-of-the-art performance on the challenging EFC dataset.

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