  Salient object detection is a fundamental problem and has been received a
great deal of attentions in computer vision. Recently deep learning model
became a powerful tool for image feature extraction. In this paper, we propose
a multi-scale deep neural network (MSDNN) for salient object detection. The
proposed model first extracts global high-level features and context
information over the whole source image with recurrent convolutional neural
network (RCNN). Then several stacked deconvolutional layers are adopted to get
the multi-scale feature representation and obtain a series of saliency maps.
Finally, we investigate a fusion convolution module (FCM) to build a final
pixel level saliency map. The proposed model is extensively evaluated on four
salient object detection benchmark datasets. Results show that our deep model
significantly outperforms other 12 state-of-the-art approaches.
