Learning Part-based Convolutional Features for Person Re-identification.

Part-level features offers fine granularity for pedestrian image description. In this article, we generally aim to learn discriminative part-informed features for person re-identification. Our contribution is two-fold. First, we introduce a general part-level feature learning method, named Part-based Convolutional Baseline (PCB). Given an image, it outputs a convolutional descriptor consisting of several part-level features. PCB is general in that it is able to accommodate several part partitioning strategies. In experiment, we show that the learned descriptor maintains a significantly higher discriminative ability than the global descriptor. Second, Based on PCB, we propose refined part pooling (RPP) to improve the original partition. Our idea is that pixels within a well-located part should be similar to each other while being dissimilar with pixels from other parts. We call it within-part consistency. When a pixel-wise feature vector in a part is more similar to some other part, it is then an outlier, indicating inappropriate partitioning. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. Experiment confirms that RPP gains another round of performance boost over PCB. For instance, on the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1 accuracy, a competitive performance with the state of the art.