Unsupervised Person Re-Identification Based on Quadratic Clustering

In view of the influence of objective factors such as hardware differences and illumination changes, the current unsupervised person re-identification method leads to a large contrast in the image of the same person, which is easy to cause the problem of wrong pseudo-labels generation of samples, which makes the existing unsupervised person re-identification method. There is still room for further improvement in the identification method. To solve this problem, this paper proposes an unsupervised person re-identification based on quadratic clustering method. This method mainly includes global quadratic clustering module and supervised learning module based on quadratic clustering results. Specifically, the former performs unsupervised analysis of camera ID and pedestrian ID based on global quadratic clustering, which solves the problem of unified imaging style of the same pedestrian under different camera perspectives; the latter uses supervised learning to improve memory. The initialization and update method of the dictionary solves the problem of model offset during training. Through the co-training of this dual module, it can jointly suppress the problem of false labels generated by images collected across cameras. The algorithm proposed in this paper is tested on Market-1501, DukeMTMC-ReID, MSMT17, Person and VeRi-776 datasets, respectively, and achieves mAP=81.2% and rank-1=91.2%, mAP=68.4% and rank-1=78.7%, mAP=31.1% and rank-1=60.4%, mAP=88.3% and rank-1=93.6%, compared with the current state-of-the-art methods, they have improved by 2.4, 1.8, 6.0, 2.5 and 4.3 percentage points rank-1 accuracy. © 2016 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.