  Visual attention is one of the most significant characteristics for selecting
and understanding the outside redundancy world. The human vision system cannot
process all information simultaneously due to the visual information
bottleneck. In order to reduce the redundant input of visual information, the
human visual system mainly focuses on dominant parts of scenes. This is
commonly known as visual saliency map prediction. This paper proposed a new
psychophysical saliency prediction architecture, WECSF, inspired by
multi-channel model of visual cortex functioning in humans. The model consists
of opponent color channels, wavelet transform, wavelet energy map, and contrast
sensitivity function for extracting low-level image features and providing a
maximum approximation to the human visual system. The proposed model is
evaluated using several datasets, including the MIT1003, MIT300, TORONTO,
SID4VAM, and UCF Sports datasets. We also quantitatively and qualitatively
compare the saliency prediction performance with that of other state-of-the-art
models. Our model achieved strongly stable and better performance with
different metrics on natural images, psychophysical synthetic images and
dynamic videos. Additionally, we found that Fourier and spectral-inspired
saliency prediction models outperformed other state-of-the-art non-neural
network and even deep neural network models on psychophysical synthetic images.
It can be explained and supported by the Fourier Vision Hypothesis. In the
meantime, we suggest that deep neural networks need specific architectures and
goals to be able to predict salient performance on psychophysical synthetic
images better and more reliably. Finally, the proposed model could be used as a
computational model of primate vision system and help us understand mechanism
of primate vision system.
