Use of permutation prefixes for efficient and scalable approximate similarity search

We present the Permutation Prefix Index (this work is a revised and extended version of Esuli (2009b), presented at the 2009 LSDS-IR Workshop, held in Boston) (PP-Index), an index data structure that supports efficient approximate similarity search.The PP-Index belongs to the family of the permutation-based indexes, which are based on representing any indexed object with “its view of the surrounding world”, i.e., a list of the elements of a set of reference objects sorted by their distance order with respect to the indexed object.In its basic formulation, the PP-Index is strongly biased toward efficiency. We show how the effectiveness can easily reach optimal levels just by adopting two “boosting” strategies: multiple index search and multiple query search, which both have nice parallelization properties.We study both the efficiency and the effectiveness properties of the PP-Index, experimenting with collections of sizes up to one hundred million objects, represented in a very high-dimensional similarity space.

Research highlights
► The Permutation Prefix Index is a data structure for approximate similarity search.
► PP-Index has good parallelization and scalability properties.
► PP-Index efficiently obtained high recall on an collection of 100 million images.
► PP-Index compares favorably against similar data structures for approximate search.