On scalable approximate search with the signature quadratic form distance

Abstract

The signature quadratic form distance and feature signatures have become a respected similarity space for effective content-based retrieval. Furthermore, the similarity space is configurable by a parameter alpha affecting both retrieval precision and intrinsic dimensionality, and thus interesting trade-offs can be achieved when a metric index is used for exact search. In this paper we combine such configurable model with state of the art approximate search techniques developed for the M-Index. In the experiments, we show that employing a configuration resulting in the best effectiveness of the measure leads also to very competitive approximate search effectiveness when using the M-Index, regardless the high intrinsic dimensionality of the corresponding similarity space. © 2013 Springer-Verlag.