  Visual Place Recognition (VPR) in areas with similar scenes such as urban or
indoor scenarios is a major challenge. Existing VPR methods using global
descriptors have difficulty capturing local specific regions (LSR) in the scene
and are therefore prone to localization confusion in such scenarios. As a
result, finding the LSR that are critical for location recognition becomes key.
To address this challenge, we introduced Patch-NetVLAD+, which was inspired by
patch-based VPR researches. Our method proposed a fine-tuning strategy with
triplet loss to make NetVLAD suitable for extracting patch-level descriptors.
Moreover, unlike existing methods that treat all patches in an image equally,
our method extracts patches of LSR, which present less frequently throughout
the dataset, and makes them play an important role in VPR by assigning proper
weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that
our approach achieved up to 6.35\% performance improvement than existing
patch-based methods.
