Name
Title | ||
|---|---|---|
Locality-Constrained Collaborative Sparse Approximation for Multiple-Shot Person Re-identification |
Abstract | ||
|---|---|---|
Person re-identification is becoming a hot research topic due to its academic importance and attractive applications in visual surveillance. This paper focuses on solving the relatively harder and more importance multiple-shot re-identification problem. Following the idea of treating it as a set-based classification problem, we propose a new model called Locality-constrained Collaborative Sparse Approximation (LCSA) which is made to be as efficient, effective and robust as possible. It improves the very recently proposed Collaborative Sparse Approximation (CSA) model by introducing two types of locality constraints to enhance the quality of the data for collaborative approximation. Extensive experiments demonstrate that LCSA is not only much better than CSA in terms of effectiveness and robustness, but also superior to other related methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ACPR.2013.14 | ACPR |
Keywords | Field | DocType |
attractive application,set-based classification problem,locality-constrained collaborative sparse approximation,hot research topic,collaborative approximation,person re-identification,importance multiple-shot re-identification problem,new model,multiple-shot person re-identification,collaborative sparse approximation,extensive experiment,academic importance,image classification,object recognition,approximation theory | Locality,Computer science,Sparse approximation,Approximation theory,Robustness (computer science),Artificial intelligence,Contextual image classification,Visual surveillance,Machine learning,Cognitive neuroscience of visual object recognition | Conference |
Citations | PageRank | References |
4 | 0.39 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Wu | 1 | 104 | 5.48 |
| Masayuki Mukunoki | 2 | 199 | 21.86 |
| Michihiko Minoh | 3 | 349 | 58.69 |