Salient object detection via region contrast and graph regularization

Xingming Wu, Mengnan Du, Weihai Chen, Jianhua Wang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Detection of salient objects in an image is now gaining increasing research interest in computer vision community. In this study, a novel region-contrast based saliency detection solution involving three phases is proposed. First, a color-based super-pixels segmentation approach is used to decompose the image into regions. Second, three high-level saliency measures which could effectively characterize the salient regions are evaluated and integrated in an effective manner to produce the initial saliency map. Finally, we construct a pairwise graphical model to encourage that adjacent image regions with similar features take continuous saliency values, thus producing the more perceptually consistent saliency map. We extensively evaluate the proposed method on three public benchmark datasets, and show it can produce promising results when compared to 14 state-of-the-art salient object detection approaches.

Original languageEnglish (US)
Article number32104
JournalScience China Information Sciences
Issue number3
StatePublished - Mar 1 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science


  • global distinctness
  • graph regularization
  • region compactness
  • region contrast
  • salient object detection


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