A Weighted Center Graph Fusion Method for Person Re-Identification

Feature fusion is widely used in person re-identification (re-ID) and has been proven effective. However, it is difficult to know which features are effective to identify a specific person and how to fuse features to explore complementary information and apply the advantages of each feature. Motivated by these problems, this paper proposes a new method of person re-ID to fuse the recognition results of multiple features at the rank level. Three innovations are included in this method: first, multiple metric spaces are constructed based on the correlation of different features to generate multiple rank results; second, the most similar candidates in each corresponding rank list is converted into a graph structure by our proposed weighted center graph (WCG), and we use an adaptive value $K$ to automatically seek the most similar images of each query, thus improving the accuracy of candidate targets. Finally, to evaluate the effect of each WCG, a discriminative power coefficient is designed and used to assign a proper coefficient for each WCG according to the discriminative power of corresponding features. The result can be obtained by re-ranking the fused WCG. The extensive experiments on five datasets demonstrate the matching rate of our proposed method by comparing with several state-of-the-art methods. Our code is available at https://github.com/gengshuze/WCG.git.