KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A two-stage cascaded foreground seeds generation for parametric min-cuts

  • Li, Shao-Mei (National Digital Switching System Engineering and Technological Research and Development Center) ;
  • Zhu, Jun-Guang (National Digital Switching System Engineering and Technological Research and Development Center) ;
  • Gao, Chao (National Digital Switching System Engineering and Technological Research and Development Center) ;
  • Li, Chun-Wei (National Digital Switching System Engineering and Technological Research and Development Center)
  • Received : 2016.03.07
  • Accepted : 2016.10.24
  • Published : 2016.11.30
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Abstract

Parametric min-cuts is an object proposal algorithm, which can be used for accurate image segmentation. In parametric min-cuts, foreground seeds generation plays an important role since the number and quality of foreground seeds have great effect on its efficiency and accuracy. To improve the performance of parametric min-cuts, this paper proposes a new framework for foreground seeds generation. First, to increase the odds of finding objects, saliency detection at multiple scales is used to generate a large set of diverse candidate seeds. Second, to further select good-quality seeds, a two-stage cascaded ranking classifier is used to filter and rank the candidates based on their appearance features. Experimental results show that parametric min-cuts using our seeding strategy can obtain a relative small pool of proposals with high accuracy.

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References

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