  As a scene graph compactly summarizes the high-level content of an image in a
structured and symbolic manner, the similarity between scene graphs of two
images reflects the relevance of their contents. Based on this idea, we propose
a novel approach for image-to-image retrieval using scene graph similarity
measured by graph neural networks. In our approach, graph neural networks are
trained to predict the proxy image relevance measure, computed from
human-annotated captions using a pre-trained sentence similarity model. We
collect and publish the dataset for image relevance measured by human
annotators to evaluate retrieval algorithms. The collected dataset shows that
our method agrees well with the human perception of image similarity than other
competitive baselines.
