Salient Object Detection Via Two-Stage Graphs

Liu, Yi and Han, Jungong and Zhang, Qiang and Wang, Long (2019) Salient Object Detection Via Two-Stage Graphs. IEEE Transactions on Circuits and Systems for Video Technology, 29 (4). 1023 - 1037. ISSN 1051-8215

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Abstract

Despite recent advances made in salient object detection using graph theory, the approach still suffers from accuracy problems when the image is characterized by a complex structure, either in the foreground or background, causing erroneous saliency segmentation. This fundamental challenge is mainly attributed to the fact that most of existing graph-based methods take only the adjacently spatial consistency among graph nodes into consideration. In this paper, we tackle this issue from a coarse-to-fine perspective and propose a two-stage-graphs approach for salient object detection, in which two graphs having the same nodes but different edges are employed. Specifically, a weighted joint robust sparse representation model, rather than the commonly used manifold ranking model, helps to compute the saliency value of each node in the first-stage graph, thereby providing a saliency map at the coarse level. In the second-stage graph, along with the adjacently spatial consistency, a new regionally spatial consistency among graph nodes is considered in order to refine the coarse saliency map, assuring uniform saliency assignment even in complex scenes. Particularly, the second stage is generic enough to be integrated in existing salient object detectors, enabling to improve their performance. Experimental results on benchmark datasets validate the effectiveness and superiority of the proposed scheme over related state-of-the-art methods.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Circuits and Systems for Video Technology
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
124625
Deposited By:
Deposited On:
16 Apr 2018 15:50
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2020 04:45