Region-Based Interactive Ranking Optimization for Person Re-identification

Person re-identification, aiming to identify images of the same person from various cameras configured in difference places, has attracted plenty of attention in the multimedia community. Previous work mainly focuses on feature presentation and distance measure, and achieves promising results on some standard databases. However, the performance is still not good enough due to appearance changes caused by variations in illuminations, poses, viewpoints and occlusion. This paper addresses the problem through result re-ranking by introducing user feedback. In particular, considering the peculiarity of scarce positive and global similar negative samples in the person re-identification problem, we propose a region-based interactive ranking optimization method, to improve the original query result by labeling locally similar and dissimilar image regions. Experiments conducted on two standard data sets have validated the effectiveness of the proposed method with an average improvement of 10-30% over original basic method. It is proved that the ranking optimization algorithm is both an effective and efficient method to improve the original person re-identification result.