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Background Cleaning and Direction Weight in Salient Object Detection

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Pattern Recognition and Computer Vision (PRCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12305))

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Abstract

Background prior is widely-used knowledge in salient object detection, and geodesic distance is based on background prior knowledge. To measure the difference between a pixel and image boundary, geodesic distance depends on a path which has the lowest cost to access the image boundary, and the cost is the value of distance. Areas with low geodesic distances are considered to be backgrounds, and the high are foregrounds. However, when some background areas are surrounded by areas that differ greatly in their colors, this best path to the boundary also needs a large cost and contributes to the high saliency of these areas. To address this problem, we propose a background cleaning (BC) method. For more ideal results in datasets, this BC method should be adaptively used. RBD (robust background detection) is a successful method in salient object detection. It is based on the global contrast information, but global contrast is easily affected by noise. In previous work, MDC (minimum directional contrast) was proposed. And we find that the directional contrast information can improve RBD method, so we use the minimum contrast information of four directions. Experimental results on two public datasets show that our method performs better than RBD, MDC and some widely-used methods.

This work was supported by National Nature Science Foundation (NNSF: 61171118, 61673234, U1636124), Scientific Research Project of Beijing Educational Committee (KM202011232014).

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Authors and Affiliations

  1. Computer School, Beijing Information Science and Technology University, Beijing, China

    Xiaodong Wang & Xiaoming Huang

Authors
  1. Xiaodong Wang
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  2. Xiaoming Huang
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Corresponding author

Correspondence to Xiaoming Huang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Wang, X., Huang, X. (2020). Background Cleaning and Direction Weight in Salient Object Detection. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_58

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  • DOI: https://doi.org/10.1007/978-3-030-60633-6_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60632-9

  • Online ISBN: 978-3-030-60633-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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