Paper Info
Title
Bi-directional Re-ranking for Person Re-identification
Abstract
For person re-identification, previous re-ranking methods focus on the unidirectional query-find-gallery ranking list and target to improve the performance of person re-identification. However, the matched images with the same identity may get lower ranks in the query-find-gallery ranking list, which limits the improvement of these re-ranking methods. To solve this problem, we propose the Bi-directional re-ranking method. Different from existing methods, we consider the bi-directional matching including the query-find-gallery ranking list and the gallery-find-query ranking list. In addition, we construct the graph of image relationship based on feature distances and expand the qualified images other than the initial top-k nearest images. By combining the bi-directional re-ranking performance and the k-neighbor similarity score, we re-rank the initial ranking list and get higher improvements. Extensive experiments show that the Bi-directional re-ranking method can facilitate the state-of-the-art person re-identification methods.
Year
DOI
Venue
2019
10.1109/MIPR.2019.00017
2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
Keywords
DocType
ISBN
Bi direction,person re identification,re ranking
Conference
978-1-7281-1198-8
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Yiqian Chang100.34
Yemin Shi2379.48
Yaowei Wang313429.62
Yonghong Tian41057102.81