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 …