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Saliency detection via combining global shape and local cue estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Recently, saliency detection has become a hot issue in computer vision. In this paper, a novel framework for image saliency detection is introduced by modeling global shape and local cue estimation simultaneously. Firstly, Quaternionic Distance Based Weber Descriptor (QDWD), which was initially designed for detecting outliers in color images, is used to model the salient object shape in an image. Secondly, we detect local saliency based on the reconstruction error by using a locality-constrained linear coding algorithm. Finally, by integrating global shape with local cue, a reliable saliency map can be computed and estimated. Experimental results, based on two widely used and openly available databases, show that the proposed method can produce reliable and promising results, compared to other state-of-the-art saliency detection algorithms.
Original languageEnglish
Title of host publicationIntelligence Science and Big Data Engineering
EditorsY. Sun, L. Zhang, J. Yang, H. Huang
Number of pages10
ISBN (Electronic)978-3319677774
ISBN (Print)978-3319677767
Publication statusPublished - 14 Sep 2017
EventIScIDE 2017: International Conference on Intelligence Science and Big Data Engineering - Dalian, China
Duration: 23 Sep 201724 Sep 2017

Publication series

NameLecture Notes in Computer Science


ConferenceIScIDE 2017: International Conference on Intelligence Science and Big Data Engineering


  • IScIDE_2017_postprint(1)

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Intelligence Science and Big Data Engineering. IScIDE 2017. LEcture Notes in Computer Science, vol 10559.. The final authenticated version is available online at:

    Accepted author manuscript (Post-print), 604 KB, PDF-document

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