# Pareto-depth for Multiple-query Image Retrieval

arXiv: https://arxiv.org/abs/1402.5176 · HTML: https://mlsanity.com/paper/1402.5176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2014.

## Built on by (most cited first, 1 of 1)

- [Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems](https://mlsanity.com/paper/2206.12993/) (2022)

## Abstract (arXiv)

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.