# Enhancing Historical Image Retrieval with Compositional Cues

arXiv: https://arxiv.org/abs/2403.14287 · HTML: https://mlsanity.com/paper/2403.14287/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Multi-modal Transformer for Video Retrieval](https://mlsanity.com/paper/2007.10639/) (2020)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [End-to-end Learning of Deep Visual Representations for Image Retrieval](https://mlsanity.com/paper/1610.07940/) (2016)
- [Deep Learning for Instance Retrieval: A Survey](https://mlsanity.com/paper/2101.11282/) (2021)
- [Composition-Aware Image Aesthetics Assessment](https://mlsanity.com/paper/1907.10801/) (2019)
- [Content-Based Video Retrieval in Historical Collections of the German Broadcasting Archive](https://mlsanity.com/paper/1702.03790/) (2017)

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

- [DGME-T: Directional Grid Motion Encoding for Transformer-Based Historical Camera Movement Classification](https://mlsanity.com/paper/2510.15725/) (2025)
- [Learning visual representations for compositional analysis of artworks and photographs](https://mlsanity.com/paper/2608.06142/) (2026)

## Abstract (arXiv)

In analyzing vast amounts of digitally stored historical image data, existing content-based retrieval methods often overlook significant non-semantic information, limiting their effectiveness for flexible exploration across varied themes. To broaden the applicability of image retrieval methods for diverse purposes and uncover more general patterns, we innovatively introduce a crucial factor from computational aesthetics, namely image composition, into this topic. By explicitly integrating composition-related information extracted by CNN into the designed retrieval model, our method considers both the image's composition rules and semantic information. Qualitative and quantitative experiments demonstrate that the image retrieval network guided by composition information outperforms those relying solely on content information, facilitating the identification of images in databases closer to the target image in human perception. Please visit https://github.com/linty5/CCBIR to try our codes.