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Salient object detection using local, global and high contrast graphs

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Abstract

In this paper, we propose a novel multi-graph-based method for salient object detection in natural images. Starting from image decomposition via a superpixel generation algorithm, we utilize color, spatial and background label to calculate edge weight matrix of the graphs. By considering superpixels as the nodes and region similarities as the edge weights, local, global and high contrast graphs are created. Then, an integration technique is applied to form the saliency maps using degree vectors of the graphs. Extensive experiments on three challenging datasets show that the proposed unsupervised method outperforms the several different state-of-the-art unsupervised methods.

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Acknowledgements

This work was supported by the Cognitive Science and Technology Council (CSTC) of Iran under the grant number 3232.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Fatemeh Nouri, Kamran Kazemi & Habibollah Danyali

Authors
  1. Fatemeh Nouri
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  2. Kamran Kazemi
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  3. Habibollah Danyali
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Correspondence to Kamran Kazemi.

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Nouri, F., Kazemi, K. & Danyali, H. Salient object detection using local, global and high contrast graphs. SIViP 12, 659–667 (2018). https://doi.org/10.1007/s11760-017-1205-5

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  • DOI: https://doi.org/10.1007/s11760-017-1205-5

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