Saliency detection using quaternionic distance based weber local descriptor and level priors

Muwei Jian, Qiang Qi, Junyu Dong, Xin Sun, Yujuan Sun, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)


In this paper, a novel and efficient framework by exploiting Quaternionic Distance Based Weber Local Descriptor (QDWLD) and object cues is proposed for image saliency detection. In contrast to the existing saliency detection models, the advantage of the proposed approach is that it can combine quaternion number system and object cues simultaneously, which is independent of image contents and scenes. Firstly, QDWLD, which was initially designed for detecting outliers in color images, is used to represent the directional cues in an image. Meanwhile, two low-level priors, namely the Convex-Hull-Based center and color contrast cue of the image, are utilized and fused as an object-level cue. Finally, by combining QDWLD with object cues, a reliable saliency map of the image can be computed and estimated. Experimental results, based on two widely used and openly available database, show that the proposed method is able to produce reliable and promising salient maps/estimations, compared to other state-of-the-art saliency-detection models.
Original languageEnglish
Pages (from-to)14343-14360
Number of pages18
JournalMultimedia Tools and Applications
Issue number11
Publication statusPublished - 1 Jun 2018


  • Directional cues
  • Object cues
  • Saliency detection
  • Weber descriptor

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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