# Structuring Quantitative Image Analysis with Object Prominence

arXiv: https://arxiv.org/abs/2409.00216 · HTML: https://mlsanity.com/paper/2409.00216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 106 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch](https://mlsanity.com/paper/1909.05845/) (2019)
- [Salient Object Detection: A Survey](https://mlsanity.com/paper/1411.5878/) (2014)
- [A Survey on Deep Learning Techniques for Stereo-based Depth Estimation](https://mlsanity.com/paper/2006.02535/) (2020)
- [Salient Object Detection: A Discriminative Regional Feature Integration Approach](https://mlsanity.com/paper/1410.5926/) (2014)
- [Model Interpretability through the Lens of Computational Complexity](https://mlsanity.com/paper/2010.12265/) (2020)
- [RGB-D Salient Object Detection: A Survey](https://mlsanity.com/paper/2008.00230/) (2020)
- [How to Make Causal Inferences Using Texts](https://mlsanity.com/paper/1802.02163/) (2018)
- [DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling](https://mlsanity.com/paper/2105.12441/) (2021)
- [TranSalNet: Towards perceptually relevant visual saliency prediction](https://mlsanity.com/paper/2110.03593/) (2021)
- [Recurrent Multi-scale Transformer for High-Resolution Salient Object Detection](https://mlsanity.com/paper/2308.03826/) (2023)
- [Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection](https://mlsanity.com/paper/2205.07179/) (2022)
- [Predicting the Politics of an Image Using Webly Supervised Data](https://mlsanity.com/paper/1911.00147/) (2019)
- [Understanding the Political Ideology of Legislators from Social Media Images](https://mlsanity.com/paper/1907.09594/) (2019)
- [Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net](https://mlsanity.com/paper/2108.07851/) (2021)
- [Model-agnostic explainable artificial intelligence for object detection in image data](https://mlsanity.com/paper/2303.17249/) (2023)
- [Size-to-depth: A New Perspective for Single Image Depth Estimation](https://mlsanity.com/paper/1801.04461/) (2018)
- [How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers](https://mlsanity.com/paper/1711.00617/) (2017)
- [Improving Computer Vision Interpretability: Transparent Two-level Classification for Complex Scenes](https://mlsanity.com/paper/2407.03786/) (2024)

## Abstract (arXiv)

When photographers or media professionals compose an image, they make deliberate choices about what to foreground and what to background, shaping how viewers interpret visual content. Yet most quantitative approaches to image analysis overlook this structure and treat detected objects as equally important. We introduce a framework for measuring object prominence-- the relative salience of objects in an image-- as a means to make computational image analysis attentive to the compositional emphasis a curator has built into an image. Drawing on research in cognitive psychology and computer vision, we outline three approaches for estimating object prominence: size and centeredness, inferred depth, and saliency maps. Validating that curator-composed prominence measurably shifts human visual attention in a pre-registered eye-tracking study, we illustrate this framework's benefits in two further applications. First, we demonstrate how weighting features in line with their prominence can enhance the unsupervised ideological scaling of U.S. newspaper images. Second, we examine gendered visual prominence in U.S. presidential campaign ads from 2016 and 2020, showing that Republican candidates depict women less prominently than their Democratic counterparts. Our framework lets researchers analyze image data at scale while remaining attentive to its communicative structure and intent.