Name
Abstract | ||
|---|---|---|
Aiming for the problem of inconsistent saliency between matched patches in person re-identification, a multi-directional salience similarity evaluation for person re-identification based on metric learning is proposed. A distribution analysis for salience consistency between the patches is taken, and the similarity between matched patches is established by weighted fusion of multi-directional salience. The weight of saliency in each direction is obtained using metric learning in the base of Structural SVM Ranking. It improves the discriminative and accuracy performance of re-identification. Compared with the similar algorithms, the method achieves higher re-identification rate with more comprehensive similarity measure. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-20904-3_5 | COMPUTER VISION SYSTEMS (ICVS 2015) |
Keywords | Field | DocType |
Person re-identification, Metric learning, Salience feature, Ranking | Computer vision,Similarity measure,Pattern recognition,Ranking,Salience (neuroscience),Computer science,Support vector machine,Artificial intelligence,Salience (language),Discriminative model,Machine learning | Conference |
Volume | ISSN | Citations |
9163 | 0302-9743 | 1 |
PageRank | References | Authors |
0.34 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhonghua Huo | 1 | 1 | 0.34 |
| Ying Chen | 2 | 19 | 3.57 |
| Chun-jian Hua | 3 | 1 | 0.34 |