  We present ElasticHash, a novel approach for high-quality, efficient, and
large-scale semantic image similarity search. It is based on a deep hashing
model to learn hash codes for fine-grained image similarity search in natural
images and a two-stage method for efficiently searching binary hash codes using
Elasticsearch (ES). In the first stage, a coarse search based on short hash
codes is performed using multi-index hashing and ES terms lookup of neighboring
hash codes. In the second stage, the list of results is re-ranked by computing
the Hamming distance on long hash codes. We evaluate the retrieval performance
of \textit{ElasticHash} for more than 120,000 query images on about 6.9 million
database images of the OpenImages data set. The results show that our approach
achieves high-quality retrieval results and low search latencies.
