Laser & Optoelectronics Progress, Volume. 56, Issue 16, 161010(2019)

Image Saliency Detection of Bayesian Integration Multi-Kernel Learning

Xuemin Chen*, Hongmei Tang, Liying Han, and Zhenbin Gao
Author Affiliations
  • School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
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    Figures & Tables(9)
    Framework of saliency detection
    Saliency test results on ECSSD and MRSA-10K datasets
    P-R curves on MSRA-10K dataset. (a) Whole curves; (b) partial curves
    F-measure curves on MSRA-10K dataset. (a) Whole curves; (b) partial curves
    P-R curves on ECSSD dataset. (a) Whole curves; (b) partial curves
    F-measure curves on ECSSD dataset. (a) Whole curves; (b) partial curves
    Best F value comparison histogram on MSRA-10K dataset
    Best F value comparison histogram on ECSSD dataset
    • Table 1. Running environments and time of various algorithms

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      Table 1. Running environments and time of various algorithms

      DatasetParameterGBVSHCGRPCADSRAMCSD-MSBLOUR
      MSRA-10KCalculation time /s13.2310.265.367.083.161.0817.9313.2312.59
      ECSSDCalculation time /s11.3313.265.907.811.371.2816.9313.5511.08
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    Xuemin Chen, Hongmei Tang, Liying Han, Zhenbin Gao. Image Saliency Detection of Bayesian Integration Multi-Kernel Learning[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161010

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    Paper Information

    Category: Image Processing

    Received: Jan. 21, 2019

    Accepted: Mar. 27, 2019

    Published Online: Aug. 5, 2019

    The Author Email: Xuemin Chen (wxmchen@163.com)

    DOI:10.3788/LOP56.161010

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