Name
Title | ||
|---|---|---|
Non-concept density estimation via kernel regression for concept ranking in weakly labelled data. |
Abstract | ||
|---|---|---|
Automatic object annotation for weakly labelled images/videos has attracted great research interests. In the literature, the idea of negative mining has been proposed for the task. Following existing works, the authors start with image/video over-segmentation. With the assumption that the noisy segments in the concept images and the strongly labelled non-concept segments are drawn from the same di... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1049/iet-cvi.2017.0036 | IET Computer Vision |
Keywords | Field | DocType |
data mining,image segmentation,maximum likelihood estimation,regression analysis,video signal processing | Density estimation,Multivariate kernel density estimation,Annotation,Pattern recognition,Ranking,Artificial intelligence,Variable kernel density estimation,Mathematics,Kernel regression,Kernel (statistics),Kernel density estimation | Journal |
Volume | Issue | ISSN |
12 | 1 | 1751-9632 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liantao Wang | 1 | 2 | 2.05 |
| Qingwu Li | 2 | 0 | 0.68 |
| Jianfeng Lu | 3 | 1 | 5.42 |
| Qiong Wang | 4 | 0 | 0.34 |