Adaptive Irregular Graph Construction-Based Salient Object Detection

Yuan Zhou, Tianhao Zhang, Shuwei Huo, Chunping Hou, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Saliency detection represents a vital pre-processing stage of computer vision. Most existing propagation-based salient object detection methods construct a k-regular graph for saliency propagation. Applying a regular graph to a vast smooth region is potentially prone to unnecessary or prolonged propagation errors, leading to the excessive highlighting of the background regions. To mitigate such problems, we substitute the conventional k-regular graph with an adaptive irregular graph for saliency value propagation, thereby avoiding unnecessary iterations over a vast smooth region. We first perform a clustering analysis based on the smoothness, color, and other features of regions. The new graph boosts an adaptive link density by considering the clustering result. In addition, we propose a seeding strategy for the propagation. Based on our experimental studies of six major benchmark datasets, our method performed favorably against the other state-of-the-art methods, both quantitatively and qualitatively.

Original languageEnglish (US)
Article number8671726
Pages (from-to)1569-1582
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number6
StatePublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering


  • Salient object detection
  • adaptive irregular graph
  • label propagation


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