Co-Saliency Detection via Hierarchical Consistency Measure

Yonghua ZHANG, Liang LI, Runmin CONG, Xiaojie GUO, Hui XU, Jiawan ZHANG*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

17 Citations (Scopus)


Co-saliency detection is a newly emerging research topic in multimedia and computer vision, the goal of which is to extract common salient objects from multiple images. Effectively seeking the global consistency among multiple images is critical to the performance. To achieve the goal, this paper designs a novel model with consideration of a hierarchical consistency measure. Different from most existing co-saliency methods that only exploit common features (such as color and texture), this paper further utilizes the shape of object as another cue to evaluate the consistency among common salient objects. More specifically, for each involved image, an intra-image saliency map is firstly generated via a single image saliency detection algorithm. Having the intra-image map constructed, the consistency metrics at object level and superpixel level are designed to measure the corresponding relationship among multiple images and obtain the inter saliency result by considering multiple visual attention features and multiple constrains. Finally, the intra-image and inter-image saliency maps are fused to produce the final map. Experiments on benchmark datasets are conducted to demonstrate the effectiveness of our method, and reveal its advances over other state-of-the-art alternatives.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Multimedia and Expo, ICME 2018 : Proceedings
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781538617373
Publication statusPublished - 2018
Externally publishedYes
Event2018 IEEE International Conference on Multimedia and Expo, ICME 2018 - San Diego, United States
Duration: 23 Jul 201827 Jul 2018

Publication series

NameProceedings : IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2018 IEEE International Conference on Multimedia and Expo, ICME 2018
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Publisher Copyright:
© 2018 IEEE.


  • Co-saliency detection
  • hierarchical consistency measure
  • multi-feature similarity
  • shape attribute


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