  This paper proposes a novel saliency detection method by combining
region-level saliency estimation and pixel-level saliency prediction with CNNs
(denoted as CRPSD). For pixel-level saliency prediction, a fully convolutional
neural network (called pixel-level CNN) is constructed by modifying the VGGNet
architecture to perform multi-scale feature learning, based on which an
image-to-image prediction is conducted to accomplish the pixel-level saliency
detection. For region-level saliency estimation, an adaptive superpixel based
region generation technique is first designed to partition an image into
regions, based on which the region-level saliency is estimated by using a CNN
model (called region-level CNN). The pixel-level and region-level saliencies
are fused to form the final salient map by using another CNN (called fusion
CNN). And the pixel-level CNN and fusion CNN are jointly learned. Extensive
quantitative and qualitative experiments on four public benchmark datasets
demonstrate that the proposed method greatly outperforms the state-of-the-art
saliency detection approaches.
