Saliency detection based on background seeds by object proposals and extended random walk

Muwei Jian, Runxia Zhao, Xin Sun, Hanjiang Luo, Wenyin Zhang, Huaxiang Zhang, Junyu Dong, Yilong Yin, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

Recently, many graph-based algorithms are applied in the research of saliency detection, which use the border of an image as a background query. This frequently leads to undesired errors and retrieval outputs when the boundaries of the salient objects concerned touch, or connect with, the image's border. In this paper, a novel bottom-up saliency-detection algorithm is proposed to tackle and overcome the above issue. First, we utilize object proposals to collect the background seeds reliably. Then, the Extended Random Walk (ERW) algorithm is adopted to propagate the prior background labels to the rest of the pixels in an image. Finally, we refine the saliency map by taking both the textural and structure-information into consideration simultaneously. Experiments on publicly available data sets show that our proposed method achieves competitive results against the state-of-the-art approaches.

Original languageEnglish
Pages (from-to)202-211
Number of pages10
JournalJournal of Visual Communication and Image Representation
Volume57
DOIs
Publication statusPublished - Nov 2018

Keywords

  • Background seeds
  • Extended random walk
  • Object proposals
  • Saliency detection

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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