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Saliency object detection: integrating reconstruction and prior

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Abstract

To remedy some challenging cases in saliency detection such as complex background and multiple objects. A new saliency object detection approach is proposed via integrating reconstruction and prior knowledge. This paper first segments each image into super pixels using over-segmentation algorithm. Then, the reconstruction saliency map and prior saliency map are generated by reconstruction and prior, respectively. The reconstruction involves dense reconstruction and sparse reconstruction. When the saliency object appears on the image boundaries, the detection can be more accurate via dense reconstruction. In addition, if there is complex background in natural scene image, the sparse reconstruction can be more robust and suppress the background effectively. The prior adopts background prior and center prior, which can highlight the saliency object uniformly. The reconstruction saliency map and prior saliency map are nonlinearly integrated to generate the final saliency map. The proposed method is compared with the other five state-of-the-art algorithms based on comprehensive metrics. The experimental results demonstrate that the proposed algorithm has superior saliency detection performance and low average elapsing time.

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Acknowledgements

This work has been supported by National Natural Science Foundation of China (61203261; 61876099), China Postdoctoral Science Foundation funded project (2012M521335), Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (MIMS16-02), Shenzhen Science and Technology Research (JCYJ20170307093018753), and Development Funds and The Fundamental Research Funds of Shandong University (2017JC043 and 2018JCG07).

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, People’s Republic of China

    Cuiping Li, Zhenxue Chen & Chengyun Liu

  2. Department of Electrical and Computer Engineering, University of Windsor, Windsor, N9B 3P4, Canada

    Q. M. Jonathan Wu

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  1. Cuiping Li
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  2. Zhenxue Chen
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  3. Q. M. Jonathan Wu
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  4. Chengyun Liu
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Correspondence to Zhenxue Chen.

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Li, C., Chen, Z., Wu, Q.M.J. et al. Saliency object detection: integrating reconstruction and prior. Machine Vision and Applications 30, 397–406 (2019). https://doi.org/10.1007/s00138-018-0995-y

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