Name
Abstract | ||
|---|---|---|
•We propose a novel boosting ensemble approach for person Re-id.•We formulate the ensemble model for person Re-id from a ranking perspective.•We utilize a rectifier loss to enhance the boosting framework to alleviate overfitting.•Experimental performance demonstrate the proposed approach achieves superior performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2019.05.022 | Pattern Recognition |
Keywords | Field | DocType |
Person re-identification,Boosting,Ensemble | Feature vector,Ranking,Pattern recognition,Artificial intelligence,Boosting (machine learning),Overfitting,Ensemble learning,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
94 | 1 | 0031-3203 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaoju Li | 1 | 1 | 1.30 |
| Zhenjun Han | 2 | 176 | 16.40 |
| Junliang Xing | 3 | 1193 | 63.31 |
| Qixiang Ye | 4 | 913 | 64.51 |
| Xuehui Yu | 5 | 0 | 1.69 |
| Jianbin Jiao | 6 | 367 | 32.61 |