Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150978
Title: An iterative co-saliency framework for RGBD images
Authors: Cong, Runmin
Lei, Jianjun
Fu, Huazhu
Lin, Weisi
Huang, Qingming
Cao, Xiaochun
Hou, Chunping
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Cong, R., Lei, J., Fu, H., Lin, W., Huang, Q., Cao, X. & Hou, C. (2017). An iterative co-saliency framework for RGBD images. IEEE Transactions On Cybernetics, 49(1), 233-246. https://dx.doi.org/10.1109/TCYB.2017.2771488
Journal: IEEE Transactions on Cybernetics
Abstract: 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.
URI: https://hdl.handle.net/10356/150978
ISSN: 2168-2267
DOI: 10.1109/TCYB.2017.2771488
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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