  This paper proposes a novel saliency detection method by developing a
deeply-supervised recurrent convolutional neural network (DSRCNN), which
performs a full image-to-image saliency prediction. For saliency detection, the
local, global, and contextual information of salient objects is important to
obtain a high quality salient map. To achieve this goal, the DSRCNN is designed
based on VGGNet-16. Firstly, the recurrent connections are incorporated into
each convolutional layer, which can make the model more powerful for learning
the contextual information. Secondly, side-output layers are added to conduct
the deeply-supervised operation, which can make the model learn more
discriminative and robust features by effecting the intermediate layers.
Finally, all of the side-outputs are fused to integrate the local and global
information to get the final saliency detection results. Therefore, the DSRCNN
combines the advantages of recurrent convolutional neural networks and
deeply-supervised nets. The DSRCNN model is tested on five benchmark datasets,
and experimental results demonstrate that the proposed method significantly
outperforms the state-of-the-art saliency detection approaches on all test
datasets.
