title: Aggregating local image descriptors into compact codes.
abstract: This paper addresses the problem of large-scale image search. Three constraints have to be taken into account: search accuracy, efficiency, and memory usage. We first present and evaluate different ways of aggregating local image descriptors into a vector and show that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension. We then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation. The evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy. Searching a 100 million image data set takes about 250 ms on one processor core.
authors.affiliation: INRIA, Campus de Beaulieu, 35042 Rennes, France. herve.jegou@inria.fr
authors.forename: Hervé
authors.initials: H
authors.lastname: Jégou
authors.forename: Florent
authors.initials: F
authors.lastname: Perronnin
authors.forename: Matthijs
authors.initials: M
authors.lastname: Douze
authors.forename: Jorge
authors.initials: J
authors.lastname: Sánchez
authors.forename: Patrick
authors.initials: P
authors.lastname: Pérez
authors.forename: Cordelia
authors.initials: C
authors.lastname: Schmid