  When considering person re-identification (re-ID) as a retrieval process,
re-ranking is a critical step to improve its accuracy. Yet in the re-ID
community, limited effort has been devoted to re-ranking, especially those
fully automatic, unsupervised solutions. In this paper, we propose a
k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is
that if a gallery image is similar to the probe in the k-reciprocal nearest
neighbors, it is more likely to be a true match. Specifically, given an image,
a k-reciprocal feature is calculated by encoding its k-reciprocal nearest
neighbors into a single vector, which is used for re-ranking under the Jaccard
distance. The final distance is computed as the combination of the original
distance and the Jaccard distance. Our re-ranking method does not require any
human interaction or any labeled data, so it is applicable to large-scale
datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW
datasets confirm the effectiveness of our method.
