Name
Abstract | ||
|---|---|---|
Multiple-shot person re-identification tackles the problem to build the correspondences between sets of human images obtained from distributed cameras. It is challenging due to large within-class variations and small between-class differences, caused by the changing of human appearance and environment. Existing methods for addressing this issue include designing the representation to capture the within-set correlation, or crafting the measure to explore the between-set separation. This paper proposes a novel set based matching model called “Locality Based Discriminative Measure (LBDM)”, in which the discriminative potentiality of a new set-to-set distance is exploited by using the learned local metric field. As experimentally demonstrated, the proposal remarkably outperforms state-of-the-art schemes on public benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/AVSS.2013.6636658 | AVSS |
Keywords | Field | DocType |
video signal processing,within-class variations,image representation,human images,multiple-shot person reidentification,image matching,within-set correlation capture,learning (artificial intelligence),between-set separation measure,locality based discriminative measure,set-to-set distance,small between-class differences,local metric field learning,distributed cameras,discriminative potentiality,set based matching model,video surveillance,learning artificial intelligence | Locality,Computer science,Image representation,Robustness (computer science),Artificial intelligence,Discriminative model,Computer vision,Pattern recognition,Image matching,Visualization,Feature extraction,Correlation,Machine learning | Conference |
Citations | PageRank | References |
4 | 0.39 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Li | 1 | 59 | 5.16 |
| Yang Wu | 2 | 6 | 0.77 |
| Masayuki Mukunoki | 3 | 199 | 21.86 |
| Michihiko Minoh | 4 | 349 | 58.69 |