Regional Relation Modeling for Visual Place Recognition

In the process of visual perception, humans perceive not only the appearance of objects existing in a place but also their relationships (e.g. spatial layout). However, the dominant works on visual place recognition are always based on the assumption that two images depict the same place if they contain enough similar objects, while the relation information is neglected. In this paper, we propose a regional relation module which models the regional relationships and converts the convolutional feature maps to the relational feature maps. We further design a cascaded pooling method to get discriminative relation descriptors by preventing the influence of confusing relations and preserving as much useful information as possible. Extensive experiments on two place recognition benchmarks demonstrate that training with the proposed regional relation module improves the appearance descriptors and the relation descriptors are complementary to appearance descriptors. When these two kinds of descriptors are concatenated together, the resulting combined descriptors outperform the state-of-the-art methods.