We present our solution to Landmark Image Retrieval Challenge 2019. This challenge was based on the large Google Landmarks Dataset V2[9]. The goal was to retrieve all database images containing the same landmark for every provided query image. Our solution is a combination of global and local models to form an initial KNN graph. We then use a novel extension of the recently proposed graph traversal method EGT [1] referred to as semi-supervised EGT to refine the graph and retrieve better candidates.
@article{arxiv.1906.04944,
title = {Semi-Supervised Exploration in Image Retrieval},
author = {Cheng Chang and Himanshu Rai and Satya Krishna Gorti and Junwei Ma and Chundi Liu and Guangwei Yu and Maksims Volkovs},
journal= {arXiv preprint arXiv:1906.04944},
year = {2019}
}