# Challenging deep image descriptors for retrieval in heterogeneous iconographic collections

arXiv: https://arxiv.org/abs/1909.08866 · HTML: https://mlsanity.com/paper/1909.08866/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Connecting Images through Time and Sources: Introducing Low-data, Heterogeneous Instance Retrieval](https://mlsanity.com/paper/2103.10729/) (2021)
- [Operational Change Detection for Geographical Information: Overview and Challenges](https://mlsanity.com/paper/2503.14109/) (2025)

## Abstract (arXiv)

This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view Permission to make digital