Name
Abstract | ||
|---|---|---|
This paper presents a novel boundary based semiautomatic tool, ByLabel, for accurate image annotation. Given an image, ByLabel first detects its edge features and computes high quality boundary fragments. Current labeling tools require the human to accurately click on numerous boundary points. ByLabel simplifies this to just selecting among the boundary fragment proposals that ByLabel automatically generates. To evaluate the performance of ByLabel, 10 volunteers, with no experiences of annotation, labeled both synthetic and real images. Compared to the commonly used tool LabelMe, ByLabel reduces image-clicks and time by 73% and 56% respectively, while improving the accuracy by 73% (from 1.1 pixel average boundary error to 0.3 pixel). The results show that our ByLabel outperforms the state-of-the-art annotation tool in terms of efficiency, accuracy and user experience. The tool is publicly available: http://webdocs.cs.ualberta.ca/similar to vis/bylabel/. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WACV.2018.00200 | 2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018) |
Field | DocType | ISSN |
Computer vision,LabelMe,User experience design,Annotation,Automatic image annotation,Pattern recognition,Computer science,Image segmentation,Pixel,Artificial intelligence,Real image | Conference | 2472-6737 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuebin Qin | 1 | 32 | 7.95 |
| Shida He | 2 | 2 | 2.73 |
| Zichen Vincent Zhang | 3 | 9 | 2.26 |
| Masood Dehghan | 4 | 49 | 7.11 |
| Martin Jägersand | 5 | 334 | 43.10 |