Name
Abstract | ||
|---|---|---|
In this work we propose a novel person re-identification approach. The solution, inspired by human gazing capabilities, wants to identify the salient regions of a given person. Such regions are used as a weighting tool in the image feature extraction process. Then, such novel representation is combined with a set of other visual features in a pairwise-based multiple metric learning framework. Finally, the learned metrics are fused to get the distance between image pairs and to re-identify a person. The proposed method is evaluated on three different benchmark datasets and compared with best state-of-the-art approaches to show its overall superior performance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-16199-0_14 | COMPUTER VISION - ECCV 2014 WORKSHOPS, PT III |
Keywords | Field | DocType |
Local Binary Pattern,Salient Region,Saliency Detection,Local Binary Pattern Feature,Camera Pair | Pairwise comparison,Computer vision,Salience (neuroscience),Computer science,Local binary patterns,Feature extraction,Artificial intelligence,A-weighting,Salient | Conference |
Volume | ISSN | Citations |
8927 | 0302-9743 | 15 |
PageRank | References | Authors |
0.57 | 42 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niki Martinel | 1 | 349 | 24.39 |
| C. Micheloni | 2 | 934 | 62.52 |
| Gian Luca Foresti | 3 | 44 | 7.06 |