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ACCV
2010
Springer

Pedestrian Recognition with a Learned Metric

11 years 4 months ago
Pedestrian Recognition with a Learned Metric
This paper presents a new method for viewpoint invariant pedestrian recognition problem. We use a metric learning framework to obtain a robust metric for large margin nearest neighbor classification with rejection (i.e., classifier will return no matches if all neighbors are beyond a certain distance). The rejection condition necessitates the use of a uniform threshold for a maximum allowed distance for deeming a pair of images a match. In order to handle the rejection case, we propose a novel cost similar to the Large Margin Nearest Neighbor (LMNN) method and call our approach Large Margin Nearest Neighbor with Rejection (LMNN-R). Our method is able to achieve significant improvement over previously reported results on the standard Viewpoint Invariant Pedestrian Recognition (VIPeR[1]) dataset.
Mert Dikmen, Emre Akbas, Thomas S. Huang, Narendra
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2010
Where ACCV
Authors Mert Dikmen, Emre Akbas, Thomas S. Huang, Narendra Ahuja
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