Area of interest adaption using feature importance

TL;DR

This paper introduces two algorithms that adapt areas of interest in eye tracking data based on feature importance, significantly improving classification accuracy and allowing better compensation for data inaccuracies, especially in complex visual tasks.

Contribution

It presents novel data-driven algorithms for adaptive AOI delineation using feature importance and gradient methods, enhancing eye tracking analysis accuracy.

Findings

Significant improvement in classification results with adaptive AOIs.

Algorithms effectively compensate for eye tracking data errors.

Applicable to qualitative analysis and complex visual stimuli.

Abstract

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