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Salient object detection via effective background prior and novel graph

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Abstract

Salient object detection is getting more and more attention in computer vision field. In this paper, we propose a novel and effective framework for salient object detection. Firstly, we develop a robust background-based map by using spatial prior to remove the foreground noises of image boundary regions. The proposed background-based map and Objectness map are integrated to obtain a coarse saliency map. Then, an effective saliency propagation mechanism is utilized to further highlight salient object and suppress background region by defining a novel graph model, each node connects to its more similar neighbors and nodes with low saliency values in the proposed graph. As a result, the coarse saliency map is optimized to the refined saliency map by novel graph based saliency propagation. Finally, we construct a novel integration framework to further integrate two saliency maps for performance improvement. Experiments on three benchmark datasets are tested, experimental results show the superiority of the proposed algorithm than other state-of-the-art methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61701101, 61973093, U1713216, 61901098, 61971118, and the Fundamental Research Fund for the Central Universities of China N2026005, N181602014, N2026004, N2026006 and N2026001.

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Authors and Affiliations

  1. Faculty of Robot Science and Engineering, Northeastern University, Shenyang, 110169, China

    Yu Pang & Chengdong Wu

  2. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Yunhe Wu

  3. School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China

    Ming Zhang

Authors
  1. Yu Pang
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  2. Yunhe Wu
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  3. Chengdong Wu
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  4. Ming Zhang
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Correspondence to Chengdong Wu or Ming Zhang.

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Pang, Y., Wu, Y., Wu, C. et al. Salient object detection via effective background prior and novel graph. Multimed Tools Appl 79, 25679–25695 (2020). https://doi.org/10.1007/s11042-020-09226-5

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  • DOI: https://doi.org/10.1007/s11042-020-09226-5

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