Unsupervised person re-identification by patch-based nearest neighbor mining

Abstract

Unsupervised person re-identification (re-ID) is still challenging due to the difficulty in learning discriminative features without labeling information. Some existing methods use the instance specificity learning to learn image features without direct supervision signals. However, this kind of instance specificity learning treats each sample as an individual instance and can not mine the inter-sample relationships, which limits the ability to explore the category boundaries. To address the unsupervised person re-ID, this paper proposes a patch-based nearest neighbor mining (PNNM) method. The proposed PNNM utilizes a patch-based feature extraction model to capture patch features from person images. Then the patch discrimination mining (PDM) is applied to supervise the model to learn a discriminative patch feature space. Meanwhile, the nearest neighbor mining (NNM) is introduced to guide the model to mine the image-level discrimination from unlabeled person images. The NNM aims to explore nearest neighbor sets with high category consistency on the whole training dataset. A two-stage training process is built with the PDM and NNM to enable the model to capture both patch-level and image-level visual clues. Extensive experiments are conducted to show the effectiveness of the proposed PNNM method in the unsupervised person re-ID task.