Name
Abstract | ||
|---|---|---|
Person re-identification means retrieving a same person in large amounts of images among disjoint camera views. An effective and robust similarity measure between a person image pair plays an important role in the re-identification tasks. In this work, we propose a new metric learning method based on least squares for person re-identification. Specifically, the similar training images pairs are used to learn a linear transformation matrix by being projected to finite discrete discriminant points using regression model; then, the metric matrix can be deduced by solving least squares problem with a closed form solution. We call it discriminant analytical least squares (DALS) metric. In addition, we develop the incremental learning scheme of DALS, which is particularly valuable in model retraining when given additional samples. Furthermore, DALS could be effectively kernelized to further improve the matching performance. Extensive experiments on the VIPeR, GRID, PRID450S and CUHK01 datasets demonstrate the effectiveness and efficiency of our approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00138-018-0917-z | Mach. Vis. Appl. |
Keywords | Field | DocType |
Person re-identification, Metric learning, Discriminant analysis, Least squares, Incremental learning, Kernel method | Least squares,Disjoint sets,Pattern recognition,Similarity measure,Regression analysis,Computer science,Discriminant,Artificial intelligence,Linear discriminant analysis,Kernel method,Grid | Journal |
Volume | Issue | ISSN |
29 | 6 | 0932-8092 |
Citations | PageRank | References |
0 | 0.34 | 35 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Zhang | 1 | 164 | 21.65 |
| Xiao Hu | 2 | 18 | 10.99 |
| Fei Dai | 3 | 15 | 4.22 |
| Jianxin Pang | 4 | 38 | 4.68 |
| Tao Jiang | 5 | 0 | 0.34 |
| Dapeng Tao | 6 | 1115 | 61.57 |