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Point-Cut: Interactive Image Segmentation Using Point Supervision

  • Changjae Oh
  • Bumsub Ham
  • Kwanghoon SohnEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)

Abstract

Interactive image segmentation is a fundamental task in many applications in graphics, image processing, and computational photography. Many leading methods formulate elaborated energy functionals, achieving high performance with reflecting human’s intention. However, they show limitations in practical usage since user interaction is labor intensive to obtain segments efficiently. We present an interactive segmentation method to handle this problem. Our approach, called point cut, requires minimal point supervision only. To this end, we use off-the-shelf object proposal methods that generate object candidates with high recall. With the single point supervision, foreground appearance can be estimated with high accuracy, and then integrated into a graph cut optimization to generate binary segments. Intensive experiments show that our approach outperforms existing methods for interactive object segmentation both qualitatively and quantitatively.

Keywords

Gaussian Mixture Model Appearance Model Salient Object Foreground Object Object Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0115-15-1007, High quality 2d-to-multiview contents generation from large-scale RGB+D database).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Yonsei UniversitySeoulRepublic of Korea

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