@inproceedings{Zhu_etal17iconip,
  title = {Deep Salient Object Detection via Hierarchical Network Learning},
  author = {D. Zhu and Y. Luo and L. Dai and X. Shao and L. Itti and J. Lu},
  abstract = {Salient object detection is a fundamental problem in both pattern recognition and image processing tasks. Previous salient object detection algorithms usually involve various features based on priors/assumptions about the properties of the objects. Inspired by the effectiveness of recently developed feature learning, we propose a novel deep salient object detection (DSOD) model using the deep residual network (ResNet 152-layers) for saliency computation. In particular, we model the image saliency from both local and global perspectives. In the local feature estimation stage, we detect local saliency by using a deep residual network (ResNet-L) which learns local region features to determine the saliency value of each pixel. In the global feature extraction stage, another deep residual network (ResNet-G) is trained to predict the saliency score of each image based on the global features. The final saliency map is generated by a conditional random field (CRF) to combining the local and global-level saliency map. Our DSOD model is capable of uniformly highlighting the objects-of-interest from complex background while well preserving object details. Quantitative and qualitative experiments on three benchmark datasets demonstrate that our DSOD method outperforms state-of-the-art methods in the salient object detection.},
  booktitle = {International Conference on Neural Information Processing},
  pages = {319-329},
  month = {Oct},
  year = {2017},
  type = {ml;bu},
  review = {full/conf},
  organization = {Springer},
}