# Area of interest adaption using feature importance

arXiv: https://arxiv.org/abs/2303.12744 · HTML: https://mlsanity.com/paper/2303.12744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 62 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction](https://mlsanity.com/paper/2002.10905/) (2020)
- [Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization tools](https://mlsanity.com/paper/1808.09296/) (2018)
- [Eye movement simulation and detector creation to reduce laborious parameter adjustments](https://mlsanity.com/paper/1804.00970/) (2018)
- [The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications](https://mlsanity.com/paper/2101.03793/) (2021)
- [HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genaration](https://mlsanity.com/paper/2201.08354/) (2022)

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

- [A Trainable Feature Extractor Module for Deep Neural Networks and Scanpath Classification](https://mlsanity.com/paper/2403.12493/) (2024)

## Abstract (arXiv)

In this paper, we present two approaches and algorithms that adapt areas of interest (AOI) or regions of interest (ROI), respectively, to the eye tracking data quality and classification task. The first approach uses feature importance in a greedy way and grows or shrinks AOIs in all directions. The second approach is an extension of the first approach, which divides the AOIs into areas and calculates a direction of growth, i.e. a gradient. Both approaches improve the classification results considerably in the case of generalized AOIs, but can also be used for qualitative analysis. In qualitative analysis, the algorithms presented allow the AOIs to be adapted to the data, which means that errors and inaccuracies in eye tracking data can be better compensated for. A good application example is abstract art, where manual AOIs annotation is hardly possible, and data-driven approaches are mainly used for initial AOIs. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FAOIGradient&mode=list