  Visual saliency is a fundamental problem in both cognitive and computational
sciences, including computer vision. In this paper, we discover that a
high-quality visual saliency model can be learned from multiscale features
extracted using deep convolutional neural networks (CNNs), which have had many
successes in visual recognition tasks. For learning such saliency models, we
introduce a neural network architecture, which has fully connected layers on
top of CNNs responsible for feature extraction at three different scales. The
penultimate layer of our neural network has been confirmed to be a
discriminative high-level feature vector for saliency detection, which we call
deep contrast feature. To generate a more robust feature, we integrate
handcrafted low-level features with our deep contrast feature. To promote
further research and evaluation of visual saliency models, we also construct a
new large database of 4447 challenging images and their pixelwise saliency
annotations. Experimental results demonstrate that our proposed method is
capable of achieving state-of-the-art performance on all public benchmarks,
improving the F- measure by 6.12% and 10.0% respectively on the DUT-OMRON
dataset and our new dataset (HKU-IS), and lowering the mean absolute error by
9% and 35.3% respectively on these two datasets.
