The overreaching goals in large-scale image retrieval are bigger, better and cheaper. For systems based on local features we show how to get both efficient geometric verification of every match and unprecedented speed for the low sparsity situation.
Large-scale systems based on quantized local features usually process the index one term at a time, forcing two separate scoring steps: First, a scoring step to find candidates with enough matches, and then a geometric verification step where a subset of the candidates are checked.
Our method searches through the index a document at a time, verifying the geometry of every candidate in a single pass. We study the behavior of several algorithms with respect to index density---a key element for large-scale databases. In order to further improve the efficiency we also introduce a new new data structure, called the counting min-tree, which outperforms other approaches when working with low database density, a necessary condition for very large-scale systems.
We demonstrate the effectiveness of our approach with a proof of concept system that can match an image against a database of more than 90~billion images in just a few seconds.