  In this paper, we propose a novel edge preserving and multi-scale contextual
neural network for salient object detection. The proposed framework is aiming
to address two limits of the existing CNN based methods. First, region-based
CNN methods lack sufficient context to accurately locate salient object since
they deal with each region independently. Second, pixel-based CNN methods
suffer from blurry boundaries due to the presence of convolutional and pooling
layers. Motivated by these, we first propose an end-to-end edge-preserved
neural network based on Fast R-CNN framework (named RegionNet) to efficiently
generate saliency map with sharp object boundaries. Later, to further improve
it, multi-scale spatial context is attached to RegionNet to consider the
relationship between regions and the global scenes. Furthermore, our method can
be generally applied to RGB-D saliency detection by depth refinement. The
proposed framework achieves both clear detection boundary and multi-scale
contextual robustness simultaneously for the first time, and thus achieves an
optimized performance. Experiments on six RGB and two RGB-D benchmark datasets
demonstrate that the proposed method achieves state-of-the-art performance.
