An Iterative co-Saliency framework 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 cosaliency 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 inter-image 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 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.
Runmin Cong, Jianjun Lei, Huazhu Fu, Qingming Huang, Xiaochun Cao, Chunping Hou, An Iterative co-Saliency framework for RGBD images, IEEE Transactions on Cybernetics, vol. 49, no. 1, pp. 233-246, 2019. [PDF]
The proposed co-saliency framework is evaluated on two RGBD benchmarks: the RGBD Cosal150 dataset  and the RGBD Coseg183 dataset :
RGBD Co-saliency Results[Download Link]
 R. Cong, J. Lei, H. Fu, Q. Huang, X. Cao, and C. Hou, "Co-saliency detection for RGBD images based on multi-constraint feature matching and cross label propagation," IEEE Transactions on Image Processing, vol. 27, no. 2, pp. 568-579, 2018. [Project]
 H. Fu, D. Xu, S. Lin, and J. Liu, “Object-based RGBD image co-segmentation with mutex constraint,” in Proc. CVPR, 2015, pp. 4428-4436. [Project]