An iterative co-saliency framework for RGBD images

Runmin CONG, Jianjun LEI*, Huazhu FU, Weisi LIN, Qingming HUANG, Xiaochun CAO, Chunping HOU

*Corresponding author for this work

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

108 Citations (Scopus)


As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD co-saliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2-D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed framework.

Original languageEnglish
Article number8116754
Pages (from-to)233-246
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number1
Early online date21 Nov 2017
Publication statusPublished - Jan 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Common probability
  • depth shape prior (DSP)
  • iterative optimization
  • RGBD co-saliency framework
  • three schemes


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