#502 · Primary category： Computer Vision
fbow
FBOW (Fast Bag of Words) is an extremmely optimized version of the DBow2/DBow3 libraries.
Project last updated：11/22/21
GitHub Stars
621
Forks
147
Contributors
3
License
MIT
Why we included this project
FBOW is a reimplementation of the DBow2/DBow3 bag-of-words libraries that trades some features for speed. Loading a vocabulary runs about 80x faster than DBOW2, and turning an image into a bag-of-words vector is roughly 6.4x faster on machines with AVX support, thanks to SIMD code paths for both binary and floating-point descriptors. It depends only on OpenCV, accepts either descriptor type out of the box, and ships a prebuilt vocabulary for ORB features, so it drops into an existing C++ vision stack without much ceremony. It also corrects a few bugs from the original DBow code. The main gap is that image indexing is not implemented yet, which makes it a better fit for retrieval and matching than for full database-backed search.
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