Abstract

Semi-Supervised Exploration in Image Retrieval

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We present our solution to Landmark Image Retrieval Challenge 2019. This challenge was based on the large Google Landmarks Dataset V2. 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 referred to as semi-supervised EGT to refine the graph and retrieve better candidates.

➢DIR concatenated with GeM trained on Landmark-v1 dataset.

➢QE and spatial verification (RANSAC) using DELF-V2

➢Novel Contribution: Semi-supervised extension of EGT for final ranking.