Integrating QDWD with pattern distinctness and local contrast for underwater saliency detection

Muwei Jian, Qiang Qi, Junyu Dong, Yilong Yin, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

90 Citations (Scopus)


In this paper, we propose a novel framework for underwater image saliency detection by exploiting Quaternionic Distance Based Weber Descriptor (QDWD), pattern distinctness, and local contrast. Our proposed algorithm incorporates quaternion number system and principal components analysis (PCA) simultaneously, so as to achieve superior performance. In our algorithm, QDWD, which was initially designed for detecting outliers in color images, is used to represent the directional cues in an underwater image. Then, PCA coordinate system is employed to compute pattern distinctness. Meanwhile, we utilize local contrast to further highlight salient regions and suppress background regions. Finally, by integrating QDWD, pattern distinctness, and local contrast, a reliable saliency map for underwater images can be computed and estimated. Experimental results, based on the publicly available OUC-VISION underwater image database, show that the proposed method can produce reliable and promising results, compared to other state-of-the-art saliency-detection models.
Original languageEnglish
Pages (from-to)31-41
Number of pages11
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - 1 May 2018


  • Local contrast
  • Pattern distinctness
  • QDWD
  • Saliency detection
  • Underwater image

ASJC Scopus subject areas

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


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