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 language | English |
|---|---|
| Title of host publication | Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers |
| Editors | Yoichi Sato, Ko Nishino, Vincent Lepetit, Shang-Hong Lai |
| Publisher | Springer Verlag |
| Pages | 229-244 |
| Number of pages | 16 |
| ISBN (Print) | 9783319541808 |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
| Event | 13th Asian Conference on Computer Vision, ACCV 2016 - Taipei, Taiwan, Province of China Duration: 2016 Nov 20 → 2016 Nov 24 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10111 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 13th Asian Conference on Computer Vision, ACCV 2016 |
|---|---|
| Country | Taiwan, Province of China |
| City | Taipei |
| Period | 16/11/20 → 16/11/24 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
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 proceeding › Conference contribution
TY - GEN
T1 - Point-cut
T2 - Interactive image segmentation using point supervision
AU - Oh, Changjae
AU - Ham, Bumsub
AU - Sohn, Kwanghoon
PY - 2017/1/1
Y1 - 2017/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85015979645&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015979645&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54181-5_15
DO - 10.1007/978-3-319-54181-5_15
M3 - Conference contribution
AN - SCOPUS:85015979645
SN - 9783319541808
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 229
EP - 244
BT - Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Sato, Yoichi
A2 - Nishino, Ko
A2 - Lepetit, Vincent
A2 - Lai, Shang-Hong
PB - Springer Verlag
ER -