  Visual place recognition is the task of recognizing a place depicted in an
image based on its pure visual appearance without metadata. In visual place
recognition, the challenges lie upon not only the changes in lighting
conditions, camera viewpoint, and scale but also the characteristic of
scene-level images and the distinct features of the area. To resolve these
challenges, one must consider both the local discriminativeness and the global
semantic context of images. On the other hand, the diversity of the datasets is
also particularly important to develop more general models and advance the
progress of the field. In this paper, we present a fully-automated system for
place recognition at a city-scale based on content-based image retrieval. Our
main contributions to the community lie in three aspects. Firstly, we take a
comprehensive analysis of visual place recognition and sketch out the unique
challenges of the task compared to general image retrieval tasks. Next, we
propose yet a simple pooling approach on top of convolutional neural network
activations to embed the spatial information into the image representation
vector. Finally, we introduce new datasets for place recognition, which are
particularly essential for application-based research. Furthermore, throughout
extensive experiments, various issues in both image retrieval and place
recognition are analyzed and discussed to give some insights into improving the
performance of retrieval models in reality.
  The dataset used in this paper can be found at
https://github.com/canhld94/Daejeon520
