Laser & Optoelectronics Progress, Volume. 56, Issue 14, 141008(2019)

Convolutional Neural Network-Based Dimensionality Reduction Method for Image Feature Descriptors Extracted Using Scale-Invariant Feature Transform

Honghao Zhou1,2, Weining Yi2, Lili Du2、*, and Yanli Qiao1,2、**
Author Affiliations
  • 1 School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, Anhui 230031, China
  • 2 Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei, Anhui 230031, China
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    Figures & Tables(10)
    Reshapeschematic of SIFT feature descriptor
    SIFT feature descriptors after changing shape. (a) Feature descriptor; (b) feature descriptor matched with Fig. 2(a); (c) feature descriptor unmatched with Fig. 2(a)
    Diagram of convolutional neural network for SIFT feature descriptor dimensionality reduction
    Partial atlases used to test matching performance of feature descriptor
    Comparison of partial matching results between CNN-SIFT feature descriptor and SIFT feature descriptor in affine transformation
    Matching performance of feature descriptors in rotation and scale transformations
    Matching performance of feature descriptors in viewpoint transformation
    Matching performance of feature descriptors in light transformation
    Average matching time of feature descriptors in Webcam dataset
    • Table 1. Structural parameters of network

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      Table 1. Structural parameters of network

      Layer12345
      Input size16×88×44×21×1281×96
      Filter size3×33×33×3--
      Number of output channel32486411
      Max-pooling size2×22×22×2--
      Nonlinearitytanhtanhtanhtanhtanh
      Out size8×44×22×11×96N
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    Honghao Zhou, Weining Yi, Lili Du, Yanli Qiao. Convolutional Neural Network-Based Dimensionality Reduction Method for Image Feature Descriptors Extracted Using Scale-Invariant Feature Transform[J]. Laser & Optoelectronics Progress, 2019, 56(14): 141008

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    Paper Information

    Category: Image Processing

    Received: Jan. 14, 2019

    Accepted: Feb. 21, 2019

    Published Online: Jul. 12, 2019

    The Author Email: Du Lili (ylqiao@aiofm.ac.cn), Qiao Yanli (lilydu@aiofm.ac.cn)

    DOI:10.3788/LOP56.141008

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