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Saliency detection via robust seed selection of foreground and background priors

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Muwei Jian
  • Ruihong Wang
  • Professor Hui Yu
  • Junyu Dong
  • Yujuan Wang
  • Yilong Yin
  • Kin-Man Lam
Recently, saliency detection has become a research hotspot in both the computer-vision and image-processing fields. Among the diverse saliency-detection approaches, those based on the foreground and background-based model can achieve promising performance. Reliable seed selection for the foreground and background priors is a critical step for successful saliency detection. In this paper, we firstly exploit the spatial distribution of the extracted directional patches to predict the centroid of each salient object in an image. Then, we adopt the located centroids as the visual-attention center of the whole image to compute the superpixel-based center prior, which can facilitate the subsequent seed selection for the foreground and background-prior generation. Finally, the two individual foreground-based and background-based saliency maps are combined together into a plausible and authentic saliency map. Extensive experimental assessments on publicly available datasets demonstrate the effectiveness of our proposed model.
Original languageEnglish
Title of host publication2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
PublisherIEEE
Pages797-801
ISBN (Electronic)978-1-7281-3248-8
ISBN (Print)978-1-7281-3249-5
DOIs
Publication statusPublished - 5 Mar 2020
Event2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference - Lanzhou, China
Duration: 18 Nov 201921 Nov 2019
http://apsipa2019.org/

Publication series

NameIEEE APSIPA ASC Proceedings Series
PublisherIEEE
ISSN (Print)2640-009X
ISSN (Electronic)2640-0103

Conference

Conference2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Abbreviated titleAPSIPA 2019
CountryChina
CityLanzhou
Period18/11/1921/11/19
Internet address

Documents

  • Saliency Detection via Robust Seed Selection _pp

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    Accepted author manuscript (Post-print), 1.39 MB, PDF document

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