ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch

• URL: http://arxiv.org/abs/2305.04710v1
• Date: Mon, 8 May 2023 13:50:47 GMT
• Title: ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch
• Authors: Nikolaus Korfhage, Markus M\"uhling, Bernd Freisleben
• Abstract summary: ElasticHash is 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.
• Score: 0.9167082845109439
• License: http://creativecommons.org/licenses/by/4.0/
• 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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