Name
Abstract | ||
|---|---|---|
In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2016.2522440 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Visualization,Image color analysis,Estimation,Principal component analysis,Computational modeling,Eigenvalues and eigenfunctions,Optimization | Salience (neuroscience),Computer science,Artificial intelligence,Discriminative model,Pairwise comparison,Computer vision,Pattern recognition,Visualization,Linear subspace,Contrast (statistics),Machine learning,Principal component analysis,Salient | Journal |
Volume | Issue | ISSN |
28 | 5 | 2162-237X |
Citations | PageRank | References |
8 | 0.46 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shu Fang | 1 | 9 | 1.15 |
| Jia Li | 2 | 524 | 42.09 |
| Yonghong Tian | 3 | 1057 | 102.81 |
| Tiejun Huang | 4 | 1281 | 120.48 |
| Xiaowu Chen | 5 | 605 | 45.05 |