Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A downside to diffusion is that it performs slowly in comparison to the naive k-NN search, which causes a non-trivial online computational cost on large datasets. To overcome this weakness, we propose a novel diffusion technique in this paper. In our work, instead of applying diffusion to the query, we pre-compute the diffusion results of each element in the database, making the online search a simple linear combination on top of the k-NN search process. Our proposed method becomes 10~ times faster in terms of online search speed. Moreover, we propose to use late truncation instead of early truncation in previous works to achieve better retrieval performance.
@article{arxiv.1811.10907,
title = {Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing},
author = {Fan Yang and Ryota Hinami and Yusuke Matsui and Steven Ly and Shin'ichi Satoh},
journal= {arXiv preprint arXiv:1811.10907},
year = {2019}
}