Explicit embeddings for nearest neighbor search with Mercer kernels

Anthony Bourrier 1, 2, 3 Florent Perronnin 4 Rémi Gribonval 2 Patrick Pérez 3 Hervé Jégou 5
1 GIPSA-VIBS - VIBS
GIPSA-DIS - Département Images et Signal
2 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
5 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : Many approximate nearest neighbor search algorithms operate under memory constraints, by computing short signatures for database vectors while roughly keeping the neighborhoods for the distance of interest. Encoding procedures designed for the Euclidean distance have attracted much attention in the last decade. In the case where the distance of interest is based on a Mercer kernel, we propose a simple, yet effective two-step encoding scheme: first, compute an explicit embedding to map the initial space into a Euclidean space; second, apply an encoding step designed to work with the Euclidean distance. Comparing this simple baseline with existing methods relying on implicit encoding, we demonstrate better search recall for similar code sizes with the chi-square kernel in databases comprised of visual descriptors, outperforming concurrent state-of-the-art techniques by a large margin.
Document type :
Journal articles
Journal of Mathematical Imaging and Vision, Springer Verlag, 2015, pp.1-10. <10.1007/s10851-015-0555-2>
Liste complète des métadonnées


https://hal.inria.fr/hal-00722635
Contributor : Anthony Bourrier <>
Submitted on : Tuesday, February 17, 2015 - 5:00:10 PM
Last modification on : Friday, February 17, 2017 - 4:10:59 PM
Document(s) archivé(s) le : Monday, May 18, 2015 - 10:45:55 AM

File

ee_ann_single.pdf
Files produced by the author(s)

Identifiers

Citation

Anthony Bourrier, Florent Perronnin, Rémi Gribonval, Patrick Pérez, Hervé Jégou. Explicit embeddings for nearest neighbor search with Mercer kernels. Journal of Mathematical Imaging and Vision, Springer Verlag, 2015, pp.1-10. <10.1007/s10851-015-0555-2>. <hal-00722635v4>

Share

Metrics

Record views

1118

Document downloads

748