A Computational Model of Eye Movements During Object Class Detection
Abstract
We present a computational model of human eye movements in an ob- ject class detection task. The model combines state-of-the-art computer vision object class detection methods (SIFT features trained using Ad- aBoost) with a biologically plausible model of human eye movement to produce a sequence of simulated fixations, culminating with the acqui- sition of a target. We validated the model by comparing its behavior to the behavior of human observers performing the identical object class detection task (looking for a teddy bear among visually complex non- target objects). We found considerable agreement between the model and human data in multiple eye movement measures, including number of fixations, cumulative probability of fixating the target, and scanpath distance.
Cite
Text
Zhang et al. "A Computational Model of Eye Movements During Object Class Detection." Neural Information Processing Systems, 2005.Markdown
[Zhang et al. "A Computational Model of Eye Movements During Object Class Detection." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/zhang2005neurips-computational/)BibTeX
@inproceedings{zhang2005neurips-computational,
title = {{A Computational Model of Eye Movements During Object Class Detection}},
author = {Zhang, Wei and Yang, Hyejin and Samaras, Dimitris and Zelinsky, Gregory J.},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {1609-1616},
url = {https://mlanthology.org/neurips/2005/zhang2005neurips-computational/}
}