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Salient object detection via compactness and objectness cues

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Abstract

Existing saliency detection algorithms are mainly patch-based. In this paper, we propose a simple but effective approach to detect salient objects by exploring both patch-level and object-level cues. First, we obtain the objectness saliency map with objectness algorithm to find potential object candidates without need of category information. Second, the compactness map is generated by measuring color spatial distribution, and then it is refined by eliminating regions connecting to the selected boundary. Finally, to enforce the consistency among salient regions, we adopt graph-based manifold ranking algorithm by constructing two graphs each using a regional property descriptor. Both qualitative and quantitative evaluations on four publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 23 state-of-the-art methods in terms of six performance criterions.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61401281 and No.41671402 and Science Foundation of Shanghai under Grant No. 14ZR1440700.

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, 201418, China

    Qing Zhang, Wenju Li, Yanjiao Shi & Guogang Cao

  2. East China University of Science and Technology, Shanghai, 200237, China

    Jiajun Lin

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  1. Qing Zhang
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  2. Jiajun Lin
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  3. Wenju Li
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  4. Yanjiao Shi
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  5. Guogang Cao
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Correspondence to Qing Zhang.

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Zhang, Q., Lin, J., Li, W. et al. Salient object detection via compactness and objectness cues. Vis Comput 34, 473–489 (2018). https://doi.org/10.1007/s00371-017-1354-0

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