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Visual saliency detection via combining center prior and U-Net

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Abstract

At present, poor background suppression is one major problem for visual saliency detection. Although many mainstream saliency detection models can effectively locate salient objects, objects in complicated backgrounds in some natural images are often mistaken for salient objects. Therefore, this paper proposed to set up a center prior-based encoder-decoder network to improve background suppression results. A traditional center prior-based method and U-Net model were combined efficiently. First, multi-scale group convolution was used to replace general convolution, which can highlight the semantic information characteristics, and high-level characteristics at the bottom of U-Net were integrated and optimized on the basis of the consideration of center prior. Then, refinements were delivered throughout the whole network by upgrading the network structure, so as to ensure the optimized features can be made full use of. Since the changes to the U-Net architecture somewhat affected the stability of the network. Therefore, branch network modules were adopted and adaptive parameters defined to coordinate the relationships between branch networks to keep the network structure well balanced. The method has been tested with four widely used databases and proven effective by comparing its results with those of another seven popular methods.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) (61976123, 61601427); Taishan Young Scholars Program of Shandong Province; and Key Development Program for Basic Research of Shandong Province (ZR2020ZD44).

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Xiangwei Lu & Muwei Jian

  2. School of Information Science and Technology, Linyi University, Linyi, China

    Muwei Jian & Xing Wang

  3. School of Creative Technologies, University of Portsmouth, Portsmouth, UK

    Hui Yu

  4. Department of Computer Science and Technology, Ocean University of China, Qingdao, China

    Junyu Dong

  5. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    Kin-Man Lam

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  1. Xiangwei Lu
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  2. Muwei Jian
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  3. Xing Wang
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  4. Hui Yu
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  5. Junyu Dong
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Correspondence to Muwei Jian.

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Communicated by A. Liu.

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Lu, X., Jian, M., Wang, X. et al. Visual saliency detection via combining center prior and U-Net. Multimedia Systems 28, 1689–1698 (2022). https://doi.org/10.1007/s00530-022-00940-8

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