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.
|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|
|Number of pages||16|
|Publication status||Published - 2017|
|Event||13th Asian Conference on Computer Vision, ACCV 2016 - Taipei, Taiwan, Province of China|
Duration: 2016 Nov 20 → 2016 Nov 24
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||13th Asian Conference on Computer Vision, ACCV 2016|
|Country||Taiwan, Province of China|
|Period||16/11/20 → 16/11/24|
Bibliographical noteFunding Information:
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).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)