Abstract. Multi-codebook quantization (MCQ) is the task of expressing a set of vectors as accurately as possible in terms of discrete entries in
multiple bases. Work in MCQ is heavily focused on lowering quantization
error, thereby improving distance estimation and recall on benchmarks
of visual descriptors at a fixed memory budget. However, recent studies
and methods in this area are hard to compare against each other, because they use different datasets, different protocols, and, perhaps most
importantly, different computational budgets.
In this work, we first benchmark a series of MCQ baselines on an equal
footing and provide an analysis of their recall-vs-running-time performance. We observe that local search quantization (LSQ) is in practice
much faster than its competitors, but is not the most accurate method
in all cases. We then introduce two novel improvements that render LSQ
(i) more accurate and (ii) faster. These improvements are easy to implement, and define a new state of the art in MCQ.