Review of visual saliency detection with comprehensive information

Runmin CONG, Jianjun LEI*, Huazhu FU, Ming-Ming CHENG, Weisi LIN, Qingming HUANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

292 Citations (Scopus)


The visual saliency detection model simulates the human visual system to perceive the scene and has been widely used in many vision tasks. With the development of acquisition technology, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. The RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. The co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of the video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discussion are conducted to provide a holistic overview of different saliency detection methods.

Original languageEnglish
Article number8466906
Pages (from-to)2941-2959
Number of pages19
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number10
Early online date16 Sept 2018
Publication statusPublished - Oct 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1991-2012 IEEE.


  • co-saliency detection
  • depth attribute
  • inter-image correspondence
  • RGBD saliency detection
  • Salient object
  • spatiotemporal constraint
  • video saliency detection


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