Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a novel multiple-query information retrieval algorithm that combines the Pareto front method (PFM) with efficient manifold ranking (EMR). We show that our proposed algorithm outperforms state of the art multiple-query retrieval algorithms on real-world image databases. We attribute this performance improvement to concavity properties of the Pareto fronts, and prove a theoretical result that characterizes the asymptotic concavity of the fronts.
@article{arxiv.1402.5176,
title = {Pareto-depth for Multiple-query Image Retrieval},
author = {Ko-Jen Hsiao and Jeff Calder and Alfred O. Hero},
journal= {arXiv preprint arXiv:1402.5176},
year = {2015}
}