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    Multi-channel and multi-scale mid-level image representation for scene classification

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    Yang_2017_JElecImaging.pdf (2.603Mb)
    Issue Date
    2017
    Author
    Yang, Jinfu
    Yang, Fei
    Wang, Guanghui
    Li, Mingai
    Publisher
    Society of Photo-optical Instrumentation Engineers (SPIE)
    Type
    Article
    Article Version
    Scholarly/refereed, publisher version
    Rights
    Copyright 2017 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
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    Abstract
    Convolutional neural network (CNN)-based approaches have received state-of-the-art results in scene classification. Features from the output of fully connected (FC) layers express one-dimensional semantic information but lose the detailed information of objects and the spatial information of scene categories. On the contrary, deep convolutional features have been proved to be more suitable for describing an object itself and the spatial relations among objects in an image. In addition, the feature map from each layer is max-pooled within local neighborhoods, which weakens the invariance of global consistency and is unfavorable to scenes with highly complicated variation. To cope with the above issues, an orderless multi-channel mid-level image representation on pre-trained CNN features is proposed to improve the classification performance. The mid-level image representation of two channels from the FC layer and the deep convolutional layer are integrated at multi-scale levels. A sum pooling approach is also employed to aggregate multi-scale mid-level image representation to highlight the importance of the descriptors beneficial for scene classification. Extensive experiments on SUN397 and MIT 67 indoor datasets demonstrate that the proposed method achieves promising classification performance.
    URI
    http://hdl.handle.net/1808/27681
    DOI
    https://doi.org/10.1117/1.JEI.26.2.023018
    Collections
    • Electrical Engineering and Computer Science Scholarly Works [302]
    Citation
    Jinfu Yang, Jinfu Yang, Fei Yang, Fei Yang, Guanghui Wang, Guanghui Wang, Mingai Li, Mingai Li, "Multi-channel and multi-scale mid-level image representation for scene classification," Journal of Electronic Imaging 26(2), 023018 (11 April 2017). https://doi.org/10.1117/1.JEI.26.2.023018

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    KU Libraries
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    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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