https://github.com/umr-ds/elastichash
Semantic Image Similarity Search in Elasticsearch
https://github.com/umr-ds/elastichash
deep-hashing elasticsearch image-similarity semantic-hashing similarity-search
Last synced: 6 months ago
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Semantic Image Similarity Search in Elasticsearch
- Host: GitHub
- URL: https://github.com/umr-ds/elastichash
- Owner: umr-ds
- License: mit
- Created: 2021-05-04T11:06:15.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-11-04T12:02:35.000Z (12 months ago)
- Last Synced: 2024-11-04T13:18:30.081Z (12 months ago)
- Topics: deep-hashing, elasticsearch, image-similarity, semantic-hashing, similarity-search
- Language: Python
- Homepage:
- Size: 3.24 MB
- Stars: 25
- Watchers: 6
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ElasticHash: Semantic Image Similarity Search in Elasticsearch
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.
More details can be found in the [slides](/ElasticHash_slides.pdf).
## Demo
https://uni-marburg.de/ntzdY
## Install
If you don't need the whole system but only the Elasticsearch index please use [elastichash](https://github.com/nik-ko/elastichash/) package:
`pip install elastichash`
## Usage
Perform image similarity search on ~7M images of the OpenImages dataset.
* Go to app directory `cd dh`
* Download model and unpack `./get_model.sh`
* Start containers:
- Run on CPU: `docker-compose up -d`
- Or, if a GPU is available, run on GPU: `docker-compose --env-file ./.env.gpu up -d`
* Download OpenImages ES index and import data into ES: `docker exec dh_app /tools/import_openimages.sh`
* Go to http://localhost
## Custom image dataset
You can use the system to perform image similarity search on a custom natural image dataset. However, for reasonable
results, this requires a directory with enough images to index.
* Go to app directory `cd dh`
* Customize `docker-compose.yaml`: Add `- path/to/mage_dir/:/app/static/images/` for a path to a folder containing images
to `app`
* Start containers `docker-compose up -d`
* Customize and run `docker exec dh_app /tools/import_dir.sh`
* Go to http://localhost
## Custom model
You can also use a custom deep hashing model (e.g. for other domains). More information on how to convert a Keras model for
ElasticHash can be found [here](/model/README.md)
## Experiments and data
Experiments can be found [here](/experiments/README.md) and datasets and models [here](https://data.uni-marburg.de/handle/dataumr/233)
## Troubleshooting
If the elasticsearch container won't start its probably a permissions issue: `sudo chown 1000:1000 -R dh/elasticsearch/data/`
## Reference
Reference to cite when you use ElasticHash in a research paper:
```
@InProceedings{korfhage2021elastichash,
title="ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch",
author={Korfhage, Nikolaus and M{\"u}hling, Markus and Freisleben, Bernd},
booktitle="Computer Analysis of Images and Patterns",
year="2021",
publisher="Springer International Publishing",
pages="14--23",
}
```
## License
ElasticHash is MIT-licensed
## Credits
The demo app uses [Natural Gallery JS](https://github.com/Ecodev/natural-gallery-js) and
[PhotoSwipe](https://photoswipe.com/) and images from the
[Open Images dataset](https://storage.googleapis.com/openimages/web/index.html)