Numerous saliency models are being developed with the use ofneural networks and are capable of combining various features and predicting
the saliency values with great results. In fact, it might be difficult to replace the possibilities of artificial intelligence applied to algorithms responsible
for predicting saliency. However, the low-level features are still important and should not be removed completely from new saliency models.
This work shows that carefully chosen and integrated features, including a deep learning based one, can be used for saliency prediction. The integration is obtained by using Multiple Kernel Learning. This solution is quite effective, as compared to a few other models tested on the same dataset.
