Saliency detection using quatemionic distance based weber descriptor and object cues

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

In this paper, a simple and efficient method, based on Quaternionic Distance Based Weber Descriptor (QDWD) and object cues, is proposed for saliency detection. Firstly, QDWD, 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 color contrast and center cue of the image, are utilized and fused as an object-level cue. Finally, by combining QDWD with object cues, a reliable saliency map of the image can be computed. Experimental results, based on a widely used and openly available database, show that the proposed method is able to produce promising results, compared to other state-of-the-art saliency-detection models.
Original languageEnglish
Title of host publication2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PublisherIEEE
ISBN (Electronic)9789881476821
DOIs
Publication statusPublished - 17 Jan 2017
Event2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of
Duration: 13 Dec 201616 Dec 2016

Conference

Conference2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Country/TerritoryKorea, Republic of
CityJeju
Period13/12/1616/12/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Signal Processing

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