Models and datasets for publication: Korfhage, N., Mühling, M., Freisleben, B. (2021). ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_2 ElasticHash uses Elasticsearch 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. The files published here are needed to set up the system for large-scale image similarity search and to reproduce the experiments. More details can be found in the Git-Repository: https://github.com/umr-ds/ElasticHash
To the files


AuthorsKorfhage, Nikolaus
ContributorsFreisleben, Bernd
Mühling, Markus
RelationshipIs Supplement To: (DOI) 10.1007/978-3-030-89131-2_2
Has Part: (URL) https://github.com/umr-ds/ElasticHash
Is Supplement To: (DOI) 10.48550/arXiv.2305.04710
LicenseCreative Commons Attribution 4.0
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NameFormatSizeChecksum (MD5)
model.zip .zip39.63Mbf0f5fe6acb9166e9f9d0c9df0ff67b83
esdata-experiments.tar.gz .gz4.915Gb7e17e47d695fe4daa8fcd22e648d81af
oi_codes.tar.gz .gz920.3Mb665d7dc0f244b270cc6777ac6b7f8d62
val_queries.zip .zip32.10Mbcfd40ceb231b6818b80ce7ed613e7a7b
license_CC-BY-4.0.txt .txt18.21Kb380b31767eeb6303e3bc300d8846f180
Creative Commons Attribution 4.0
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0