ElasticHash: Semantic Image Similarity Search in Elasticsearch
Abstract
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
Metadata
Date | 2023-05-12 |
Authors | Korfhage,
Nikolaus![]() |
Contributors | Freisleben,
Bernd![]() Mühling, Markus ![]() |
Relationship | Is 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 |
License | Creative Commons Attribution 4.0 |
Faculty | FB12:Department of Mathematics and Computer Science |
Data types | Dataset Model |
Keywords | deep hashing similarity search Elasticsearch |
URI | https://data.uni-marburg.de/handle/dataumr/233
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Files
Name | Format | Size | Checksum (MD5) |
---|---|---|---|
model.zip | .zip | 39.63Mb | f0f5fe6acb9166e9f9d0c9df0ff67b83 |
esdata-experiments.tar.gz | .gz | 4.915Gb | 7e17e47d695fe4daa8fcd22e648d81af |
oi_codes.tar.gz | .gz | 920.3Mb | 665d7dc0f244b270cc6777ac6b7f8d62 |
val_queries.zip | .zip | 32.10Mb | cfd40ceb231b6818b80ce7ed613e7a7b |
license_CC-BY-4.0.txt | .txt | 18.21Kb | 380b31767eeb6303e3bc300d8846f180 |
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0