Name
Abstract | ||
|---|---|---|
We propose a semi-automatic method to obtain foreground object masks for a large set of related images. We develop a stagewise active approach to propagation: in each stage, we actively determine the images that appear most valuable for human annotation, then revise the foreground estimates in all unlabeled images accordingly. In order to identify images that, once annotated, will propagate well to other examples, we introduce an active selection procedure that operates on the joint segmentation graph over all images. It prioritizes human intervention for those images that are uncertain and influential in the graph, while also mutually diverse. We apply our method to obtain foreground masks for over 1 million images. Our method yields state-of-the-art accuracy on the ImageNet and MIT Object Discovery datasets, and it focuses human attention more effectively than existing propagation strategies. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.313 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Field | DocType | Volume |
Computer vision,Graph,Annotation,Pattern recognition,Computer science,Segmentation,Image segmentation,Artificial intelligence | Conference | 2016 |
Issue | ISSN | Citations |
1 | 1063-6919 | 11 |
PageRank | References | Authors |
0.50 | 23 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suyog Dutt Jain | 1 | 108 | 5.48 |
| Kristen Grauman | 2 | 6258 | 326.34 |