Name
Abstract | ||
|---|---|---|
Person re-identification aims at matching individuals across multiple non-overlapping adjacent cameras. By condensing multiple gallery images of a person as a whole, we propose a novel method named Set-Label Model (SLM) to improve the performance of person re-identification under the multi-shot setting. Moreover, we utilize mutual-information to measure the relevance between query image and gallery sets. To decrease the computational complexity, we apply a Naive–Bayes Nearest-Neighbor algorithm to approximate the mutual-information value. To overcome the limitations of traditional linear metric learning, we further develop a deep non-linear metric learning (DeepML) approach based on Neighborhood Component Analysis and Deep Belief Network. To evaluate the effectiveness of our proposed approaches, SLM and DeepML, we have carried out extensive experiments on two challenging datasets i-LIDS and ETHZ. The experimental results demonstrate that the proposed methods can obtain better performances compared with the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.11.002 | Neurocomputing |
Keywords | Field | DocType |
Person re-identification,Mutual-information,Metric learning,Deep learning,Neighborhood component analysis | Deep belief network,Neighborhood component analysis,Artificial intelligence,Mutual information,Deep learning,Mathematics,Machine learning,Computational complexity theory | Journal |
Volume | ISSN | Citations |
151 | 0925-2312 | 19 |
PageRank | References | Authors |
0.84 | 25 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Liu | 1 | 113 | 10.67 |
| Bingpeng Ma | 2 | 651 | 36.63 |
| Lei Qin | 3 | 515 | 27.67 |
| Junbiao Pang | 4 | 193 | 15.81 |
| Chunjie Zhang | 5 | 482 | 39.70 |
| Qingming Huang | 6 | 3919 | 267.71 |