Open AccessLooking around in the neighbourhood: Location estimation of outdoor urban images
Author(s) -
Huang Jie,
Huang Haozhi
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12190
Subject(s) - computer science , ranking (information retrieval) , artificial intelligence , image retrieval , image (mathematics) , similarity (geometry) , neighbourhood (mathematics) , pattern recognition (psychology) , feature extraction , estimation , data mining , computer vision , mathematics , mathematical analysis , management , economics
Visual geolocalisation has remained as a challenge in the research community: Given a query image, and a geo‐tagged reference database, the goal is to derive a location estimate for the query image. We propose an approach to tackling the geolocalisation problem in a four‐step manner. Essentially, our approach focuses on re‐ranking the candidate images after image retrieval, by considering the visual similarity of the candidate and its neighbouring images, to the query image. By introducing the neighbouring images, the visual information of a candidate location has been enriched. The evaluation has been conducted on three street view datasets, where our approach outperforms three baseline approaches, in terms of location estimation accuracy on two datasets. We provide discussions related to, firstly, whether using deep features for image retrieval helps improve location estimation accuracy, and the effectiveness of geographical neighbourhoods; secondly, using different deep architectures for feature extraction, and its impact on estimation accuracy; thirdly, investigating if our approach consistently outperforms the classic 1‐NN approach, on two datasets with significant difference in visual elements.