Linkage-Based Object Re-Identification via Graph Learning

Object Re-identification (Re-ID), which includes person Re-ID and vehicle Re-ID, is one of the core technologies of the intelligent transportation system. Existing supervised Re-ID studies mainly focus on discriminative feature learning (e.g., attention-based methods) or metric learning (e.g., triplet-loss-based methods) to obtain more accurate matches between the probe object and the positive gallery. However, they both pay less attention to global structure information (GSI) buried in the overall datasets. In this paper, we go beyond the traditional methods that are either unaware of or locally perceiving to GSI, and consider exploring the structural relationships among all the object instances of a dataset via a graph. Specifically, we construct a graph across the entire dataset, where each object instance is treated as a node and edges are assigned with the help of a classic algorithm like KNN. Seeing that a binary edge label can be used to predict whether its associated nodes belong to the same identity, we naturally formulate the problem of Re-ID as a new link prediction problem. Inspired by the superior capacity of capturing structure information of graph convolutional networks (GCN), a GCN-based global structure embedded network (GSE-Net) is proposed to take the graph as input and output a set of linkage likelihoods. During testing, we perform the evaluation according to the node features or estimated linkage likelihood via a graph where nodes include query and gallery images. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-arts on both person and vehicle Re-ID benchmarks.