Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection

Zukai Chen; Xin Tan; Hengliang Zhu; Shouhong Ding; Lizhuang Ma; Haichuan Song

ICASSP 2019accepted0 citations

Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection

Zukai Chen, Xin Tan, Hengliang Zhu, Shouhong Ding, Lizhuang Ma, Haichuan Song

Abstract

With the development of convolutional neural networks (CNNs), saliency detection methods have made a big progress in recent years. However, the previous methods sometimes mistakenly highlight the non-salient region, especially in complex backgrounds. To solve this problem, a two-stage method for saliency detection is proposed in this paper. In the first stage, a network is used to regress the minimum salient region (RMSR) containing all salient objects. Then in the second stage, in order to fuse the multi-level features, the spiral sharing network (SSN) is proposed for pixel-level detection on the result of RMSR. Experimental results on four public datasets show that our model is effective over the state-of-the-art approaches.

BibTeX

@inproceedings{icassp2019_learningthespira,
  title = {Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection},
  author = {Zukai Chen and Xin Tan and Hengliang Zhu and Shouhong Ding and Lizhuang Ma and Haichuan Song},
  booktitle = {ICASSP 2019},
  year = {2019}
}