Semi-supervised salient object detection using a linear feedback control system model

Yuan Zhou, Shuwei Huo, Wei Xiang, Chunping Hou, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


To overcome the challenging problems in saliency detection, we propose a novel semi-supervised classifier which makes good use of a linear feedback control system (LFCS) model by establishing a relationship between control states and salient object detection. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which are regarded as the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using an LFCS model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. This paper also covers comprehensive simulation study based on public datasets, which demonstrates the superiority of the proposed approach.

Original languageEnglish (US)
Article number8334814
Pages (from-to)1173-1185
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number4
StatePublished - Apr 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


  • Linear control system
  • Saliency detection
  • Semi-supervised learning


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