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https://github.com/enet4/bag-of-colors-nb
Bag of Colors implementation in notebook form
https://github.com/enet4/bag-of-colors-nb
bag-of-visual-words computer-vision feature-extraction image-analysis
Last synced: 5 days ago
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Bag of Colors implementation in notebook form
- Host: GitHub
- URL: https://github.com/enet4/bag-of-colors-nb
- Owner: Enet4
- License: mit
- Created: 2018-05-21T18:29:26.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-05-21T18:30:00.000Z (over 6 years ago)
- Last Synced: 2024-11-07T23:46:23.758Z (about 2 months ago)
- Topics: bag-of-visual-words, computer-vision, feature-extraction, image-analysis
- Language: Jupyter Notebook
- Size: 5.86 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
# Bags of Colors
This is a personal, unofficial implementation of bags of colors for image descriptors. It is based on the following paper:
> Christian Wenger, Matthijs Douze, Hervé Jégou, \"Bag-of-colors for improved image search\". Online:
### Using
Serve the notebook file with [Jupyter](http://jupyter.org). Image data sets need to be obtained and prepared separately, and some constants may have to be updated accordingly.
In order to prepare the data sets, create two directories, `training_data` and `testing_data`, each containing nothing but standard image files (e.g. PNG). A substantial amount of images is recommended (>10k).
### Requirements
Python 3 is required. Aside from the given Python dependencies in [requirements.txt](requirements.txt), the Python bindings for [Faiss](https://github.com/facebookresearch/faiss) must also be installed in the system. Please follow the [instructions](https://github.com/facebookresearch/faiss/blob/master/INSTALL.md#step-2-compiling-the-python-interface) in the official repository on building Faiss for Python. GPU support is recommended, but the code can be easily adjusted to function without it.
### License
MIT