  Human vision is naturally more attracted by some regions within their field
of view than others. This intrinsic selectivity mechanism, so-called visual
attention, is influenced by both high- and low-level factors; such as the
global environment (illumination, background texture, etc.), stimulus
characteristics (color, intensity, orientation, etc.), and some prior visual
information. Visual attention is useful for many computer vision applications
such as image compression, recognition, and captioning. In this paper, we
propose an end-to-end deep-based method, so-called SALYPATH (SALiencY and
scanPATH), that efficiently predicts the scanpath of an image through features
of a saliency model. The idea is predict the scanpath by exploiting the
capacity of a deep-based model to predict the saliency. The proposed method was
evaluated through 2 well-known datasets. The results obtained showed the
relevance of the proposed framework comparing to state-of-the-art models.
