  Visual Attention Prediction (VAP) is a significant and imperative issue in
the field of computer vision. Most of existing VAP methods are based on deep
learning. However, they do not fully take advantage of the low-level contrast
features while generating the visual attention map. In this paper, a novel VAP
method is proposed to generate visual attention map via bio-inspired
representation learning. The bio-inspired representation learning combines both
low-level contrast and high-level semantic features simultaneously, which are
developed by the fact that human eye is sensitive to the patches with high
contrast and objects with high semantics. The proposed method is composed of
three main steps: 1) feature extraction, 2) bio-inspired representation
learning and 3) visual attention map generation. Firstly, the high-level
semantic feature is extracted from the refined VGG16, while the low-level
contrast feature is extracted by the proposed contrast feature extraction block
in a deep network. Secondly, during bio-inspired representation learning, both
the extracted low-level contrast and high-level semantic features are combined
by the designed densely connected block, which is proposed to concatenate
various features scale by scale. Finally, the weighted-fusion layer is
exploited to generate the ultimate visual attention map based on the obtained
representations after bio-inspired representation learning. Extensive
experiments are performed to demonstrate the effectiveness of the proposed
method.
