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The Visual Computer

, Volume 35, Issue 3, pp 385–398 | Cite as

SSG: superpixel segmentation and GrabCut-based salient object segmentation

  • Xianen Zhou
  • Yaonan Wang
  • Qing ZhuEmail author
  • Changyan Xiao
  • Xiao Lu
Original Article
  • 269 Downloads

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.

Keywords

Salient object segmentation Superpixel segmentation GrabCut Region growing DBSCAN clustering 

Notes

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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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Engineering Laboratory for Robot Visual Perception and Control TechnologyHunan UniversityChangshaChina
  2. 2.Hunan Normal UniversityChangshaChina

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