TY  - CONF
AU  - David Berga
AU  - Xose R. Fernandez-Vidal
AU  - Xavier Otazu
AU  - Xose M. Pardo
A2  - ICCV
PY  - 2019//
TI  - SID4VAM: A Benchmark Dataset with Synthetic Images for Visual Attention Modeling
BT  - 18th IEEE International Conference on Computer Vision
SP  - 8788
EP  - 8797
N2  - A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor popout type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-ofthe-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets.
UR  - https://ieeexplore.ieee.org/document/9008799
L1  - http://refbase.cvc.uab.es/files/BFO2019b.pdf
UR  - http://dx.doi.org/10.1109/ICCV.2019.00888
N1  - NEUROBIT; 600.128
ID  - David Berga2019
ER  -