Point-cut: Interactive image segmentation using point supervision

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers
EditorsYoichi Sato, Ko Nishino, Vincent Lepetit, Shang-Hong Lai
PublisherSpringer Verlag
Pages229-244
Number of pages16
ISBN (Print)9783319541808
DOIs
Publication statusPublished - 2017 Jan 1
Event13th Asian Conference on Computer Vision, ACCV 2016 - Taipei, Taiwan, Province of China
Duration: 2016 Nov 202016 Nov 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10111 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th Asian Conference on Computer Vision, ACCV 2016
CountryTaiwan, Province of China
City Taipei
Period16/11/2016/11/24

Fingerprint

Photography
Image segmentation
Image Segmentation
Image processing
Personnel
Segmentation
Experiments
Graph Cuts
User Interaction
Image Processing
High Accuracy
High Performance
Binary
Optimization
Energy
Experiment
Object

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Oh, C., Ham, B., & Sohn, K. (2017). Point-cut: Interactive image segmentation using point supervision. In Y. Sato, K. Nishino, V. Lepetit, & S-H. Lai (Eds.), Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers (pp. 229-244). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10111 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-54181-5_15
Oh, Changjae ; Ham, Bumsub ; Sohn, Kwanghoon. / Point-cut : Interactive image segmentation using point supervision. Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers. editor / Yoichi Sato ; Ko Nishino ; Vincent Lepetit ; Shang-Hong Lai. Springer Verlag, 2017. pp. 229-244 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Oh, C, Ham, B & Sohn, K 2017, Point-cut: Interactive image segmentation using point supervision. in Y Sato, K Nishino, V Lepetit & S-H Lai (eds), Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10111 LNCS, Springer Verlag, pp. 229-244, 13th Asian Conference on Computer Vision, ACCV 2016, Taipei, Taiwan, Province of China, 16/11/20. https://doi.org/10.1007/978-3-319-54181-5_15

Point-cut : Interactive image segmentation using point supervision. / Oh, Changjae; Ham, Bumsub; Sohn, Kwanghoon.

Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers. ed. / Yoichi Sato; Ko Nishino; Vincent Lepetit; Shang-Hong Lai. Springer Verlag, 2017. p. 229-244 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10111 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

AB - 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.

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Oh C, Ham B, Sohn K. Point-cut: Interactive image segmentation using point supervision. In Sato Y, Nishino K, Lepetit V, Lai S-H, editors, Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers. Springer Verlag. 2017. p. 229-244. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-54181-5_15