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SSG: superpixel segmentation and GrabCut-based salient object segmentation

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Abstract

Saliency detection is a popular topic for image processing recently. In this paper, we propose a simple, robust and fast salient object segmentation framework. Firstly, we develop a novel saliency map segmentation strategy, named SSG which consists of superpixel region growing, superpixel Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering and iterated graph cuts (GrabCut), where DBSCAN makes similar background regions cluster as a whole, region growing groups similar regions together as much as possible, GrabCut segments salient objects accurately. Then, the proposed SSG is combined with saliency detection to abstract salient objects. Experimental results on three benchmark datasets demonstrate that the proposed method achieves the favorable performance than many recent state-of-the-art methods in terms of precision, recall, F-measure and execution time.

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Notes

  1. http://pan.baidu.com/s/1sl8YrXN, download code: 28uq.

  2. http://www.cs.bu.edu/groups/ivc/fastMBD/.

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Acknowledgements

This work was supported by the National Science Foundation of China (61573134, 61703155), the National Science and Technology Support Program (2015BAF13B00) and the Innovation Project of Postgraduate Student in Hunan Province, China (CX2017B108).

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Authors and Affiliations

  1. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China

    Xianen Zhou, Yaonan Wang, Qing Zhu & Changyan Xiao

  2. Hunan Normal University, Changsha, 410082, China

    Xiao Lu

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  1. Xianen Zhou
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  2. Yaonan Wang
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  3. Qing Zhu
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  4. Changyan Xiao
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Correspondence to Qing Zhu.

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Zhou, X., Wang, Y., Zhu, Q. et al. SSG: superpixel segmentation and GrabCut-based salient object segmentation. Vis Comput 35, 385–398 (2019). https://doi.org/10.1007/s00371-018-1471-4

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