A joint compression scheme for local binary feature descriptors and their corresponding bag-of-words representation

For real-time computer vision tasks, binary feature descriptors are an efficient alternative to their real-valued counterparts. While providing comparable results for many applications, the computational complexity of extracting and processing binary descriptors is significantly lower. In many application scenarios, the local features are transmitted over a channel with limited capacity and processed at a more powerful central processing unit, which requires efficient compression and transmission approaches. In this paper, we present a compression scheme for local binary features, which jointly encodes the descriptors and their respective Bag-of-Words representation using a shared vocabulary between client and server. By sending the visual word index and the entropy-coded residual vector containing the differences between the visual word and the descriptor, we are able to reduce ORB features to 60.62 % of their uncompressed size.