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arXiv · 2305.04710

ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch

Nikolaus Korfhage·Markus Mühling·Bernd Freisleben
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Abstract

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.

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Nikolaus Korfhage, Markus Mühling, Bernd Freisleben. 2023-05-08. ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch. https://doi.org/10.1007/978-3-030-89131-2_2

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