# Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?

arXiv: https://arxiv.org/abs/1608.04042 · HTML: https://mlsanity.com/paper/1608.04042/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2016.

## Built on by (most cited first, 8 of 8)

- [PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution](https://mlsanity.com/paper/2403.07589/) (2024)
- [Peripheral Vision Transformer](https://mlsanity.com/paper/2206.06801/) (2022)
- [On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation](https://mlsanity.com/paper/2112.07173/) (2021)
- [Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers](https://mlsanity.com/paper/2507.15833/) (2025)
- [Predicting Reaction Time to Comprehend Scenes with Foveated Scene Understanding Maps](https://mlsanity.com/paper/2505.12660/) (2025)
- [Noise-Aware Video Saliency Prediction](https://mlsanity.com/paper/2104.08038/) (2021)
- [Robusto-1 Dataset: Comparing Humans and VLMs on real out-of-distribution Autonomous Driving VQA from Peru](https://mlsanity.com/paper/2503.07587/) (2025)
- [FAVE: Foveated Adaptive Visual Encoding for Efficient Fine-Grained Visual Understanding](https://mlsanity.com/paper/2609.04392/) (2026)

## Abstract (arXiv)

Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. However, most models do not take into account the fact that the effects of clutter interact with the foveated nature of the human visual system: visual clutter further from the fovea has an increasing detrimental influence on perception. Here, we introduce a new foveated clutter model to predict the detrimental effects in target search utilizing a forced fixation search task. We use Feature Congestion (Rosenholtz et al.) as our non foveated clutter model, and we stack a peripheral architecture on top of Feature Congestion for our foveated model. We introduce the Peripheral Integration Feature Congestion (PIFC) coefficient, as a fundamental ingredient of our model that modulates clutter as a non-linear gain contingent on eccentricity. We finally show that Foveated Feature Congestion (FFC) clutter scores r(44) = -0.82 correlate better with target detection (hit rate) than regular Feature Congestion r(44) = -0.19 in forced fixation search. Thus, our model allows us to enrich clutter perception research by computing fixation specific clutter maps. A toolbox for creating peripheral architectures: Piranhas: Peripheral Architectures for Natural, Hybrid and Artificial Systems will be made available.