Name
Abstract | ||
|---|---|---|
Re-identifying the same person in different images is a distinct challenge for visual surveillance systems. Building an accurate correspondence between highly variable images requires a suitable dissimilarity measure. To date, most existing measures have used adapted distance based on a learned metric. Unfortunately, real-world human image data, which tends to show large intra-class variations and small inter-class differences, continues to prevent these measures from achieving satisfactory re-identification performance. Recognizing neighboring distribution can provide additional useful information to help tackle the deviation of the to-be-measured samples, we propose a novel dissimilarity measure from the neighborhood-wise relative information perspective, which can deliver the effectiveness of those well-distributed samples to the badly-distributed samples to make intra-class dissimilarities smaller than inter-class dissimilarities, in a learned discriminative space. The effectiveness of this method is demonstrated by explanation and experimentation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1587/transinf.2014EDP7102 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
person re-identification, common-near-neighbor analysis, learned metric, visual surveillance | Pattern recognition,Computer science,Artificial intelligence,Visual surveillance,Discriminative model,Machine learning | Journal |
Volume | Issue | ISSN |
E97D | 11 | 1745-1361 |
Citations | PageRank | References |
3 | 0.37 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Li | 1 | 59 | 5.16 |
| Masayuki Mukunoki | 2 | 199 | 21.86 |
| Yinghui Kuang | 3 | 3 | 0.37 |
| Yang Wu | 4 | 104 | 5.48 |
| Michihiko Minoh | 5 | 349 | 58.69 |