Abstract

I will present a new data structure for efficient similarity search in very large datasets of high-dimensional vectors. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and pre-processing time, inverted multi-indices achieve a much denser subdivision of the search space compared to inverted indices, while retaining their memory efficiency. Our experiments with large datasets of SIFT and GIST vectors demonstrate that because of the denser subdivision, inverted multi-indices are able to return much shorter candidate lists with higher recall. Augmented with a suitable reranking procedure, multi-indices were able to improve the speed of approximate nearest neighbor search on the dataset of 1 billion SIFT vectors by an order of magnitude compared to the best previously published systems, while achieving better recall and incurring only few percent of memory overhead. This is a joint work with Artem Babenko.