Evolution of ICTs-empowered-identification: A general re-ranking method for person re-identification

Abstract Re-ranking is becoming a critical part of retrieved based Person re-identification algorithms. Existing re-ranking methods always require lots of queries and memory to go through the k -nearest neighbors. To solve this problem, we introduce a Feature Relation Map (FRM) to mine the latent relation between the k -neighbors through convolution neural network, and propose a metric learning based Similarity Evaluation (SE) model to obtain the re-ranking distance from the FRM. The dilated convolution is then introduced by concatenating the dilated convolution kernels and normal convolution kernels along the channel dimension in the improvement of the SE model (named SE-d model), to allow the SE model to efficiently compare more samples pairs for obtaining the final re-ranking distance. Further, we embedding out FRM-SE model to the existing re-ranking methods to prove the effectiveness of our re-ranking model. The experiments on Market1501, CUHK03, DukeMTMC and MSMT17 benchmarks illustrates the superiority of proposed method to the state-of-the-art re-ranking methods. Although the SE model make a great improve performance, the SE-d model gains an increase of 0.31%, 0.63% in top-1 respectably compared with the SE model in the DukeMTMC and MSMT17 datasets. Meanwhile, the SE-d model speed up the model convergence and shorten the training time. Furthermore, in the transfer learning setting, the model trained on either Market1501, CUHK03 or DukeMTMC can achieve a comparable accuracy improvement on the MSMT17 dataset, which validates the generalization of our SE model.