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Query-Adaptative Locality Sensitive Hashing
Abstract : It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal processing methods suffer from this computing cost. Dramatic performance gains can be obtained by using approximate search, such as the popular Locality-Sensitive Hashing. This paper improves LSH by performing an on-line selection of the most appropriate hash functions from a pool of functions. An additional improvement originates from the use of $E_8$ lattices for geometric hashing instead of one-dimensional random projections. A performance study based on state-of-the-art high-dimensional descriptors computed on real images shows that our improvements to LSH greatly reduce the search complexity for a given level of accuracy.
https://hal.inria.fr/inria-00318614
Contributor : Hervé Jégou <>
Submitted on : Tuesday, March 15, 2011 - 2:51:35 PM
Last modification on : Thursday, March 26, 2020 - 8:49:27 PM
Document(s) archivé(s) le : Thursday, November 8, 2012 - 11:45:18 AM
Contributor : Hervé Jégou <>
Submitted on : Tuesday, March 15, 2011 - 2:51:35 PM
Last modification on : Thursday, March 26, 2020 - 8:49:27 PM
Document(s) archivé(s) le : Thursday, November 8, 2012 - 11:45:18 AM