Semi-Supervised Ranking for Re-identification with Few Labeled Image Pairs

In many person re-identification applications, typically only a small number of labeled image pairs are available for training. To address this serious practical issue, we propose a novel semi-supervised ranking method which makes use of unlabeled data to improve the reidentification performance. It is shown that low density separation or graph propagation assumption is not valid under some conditions in person re-identification. Thus, we propose to iteratively select the most confident matched (positive) image pairs from the unlabeled data. Since the number of positive matches is greatly smaller than that of negative ones, we increase the positive prior by selecting positive data from the topranked matching subset among all unlabeled data. The optimal model is learnt by solving a regression based ranking problem. Experimental results show that our method significantly outperforms state-of-the-art distance learning algorithms on three publicly available datasets using only few labeled matched image pairs for training.