  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.
