  In this paper, we proposed an integrated model of semantic-aware and
contrast-aware saliency combining both bottom-up and top-down cues for
effective saliency estimation and eye fixation prediction. The proposed model
processes visual information using two pathways. The first pathway aims to
capture the attractive semantic information in images, especially for the
presence of meaningful objects and object parts such as human faces. The second
pathway is based on multi-scale on-line feature learning and information
maximization, which learns an adaptive sparse representation for the input and
discovers the high contrast salient patterns within the image context. The two
pathways characterize both long-term and short-term attention cues and are
integrated dynamically using maxima normalization. We investigate two different
implementations of the semantic pathway including an End-to-End deep neural
network solution and a dynamic feature integration solution, resulting in the
SCA and SCAFI model respectively. Experimental results on artificial images and
5 popular benchmark datasets demonstrate the superior performance and better
plausibility of the proposed model over both classic approaches and recent deep
models.
