Abstract | ||
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AbstractAbstractAlthough methods based on the fully convolutional neural networks (FCNs) have shown strong advantages in the field of salient object detection, the existing methods still have two challenging issues: insufficient multi-level feature fusion ability and boundary blur. To overcome these issues, we propose a novel salient object detection method based on a multi-feature fusion cross network (denoted MFC-Net). Firstly, to overcome the issue of insufficient multi-level feature fusion ability, inspired by the connection mode of human brain neurons, we propose a novel cross network framework, combined with contextual feature transfer modules (CFTMs) to integrate, enhance and transmit multi-level feature information in an iterative manner. Secondly, to address the issue of blurred boundaries, we effectively enhance the edge features of saliency map by a simple edge enhancement strategy. Thirdly, to reduce the loss of information caused by the saliency map generated by multi-level feature fusion, we use feature fusion modules (FFMs) to learn contextual feature information from multiple angles and then output the resulting saliency map. Finally, a hybrid loss function fully supervises the network at the pixel and object level, optimizing the network performance. The proposed MFC-Net has been evaluated using five benchmark datasets. The performance evaluation demonstrates that the proposed method outperforms other state-of-the-art methods, which proves the superiority of MFC-Net approach. |
Year | DOI | Venue |
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2021 | 10.1016/j.imavis.2021.104243 | Periodicals |
Keywords | DocType | Volume |
Salient object detection, Cross network framework, Contextual feature transfer, Feature fusion | Journal | 113 |
Issue | ISSN | Citations |
C | 0262-8856 | 1 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhenyu Wang | 1 | 1 | 0.37 |
Yunzhou Zhang | 2 | 219 | 30.98 |
Yan Liu | 3 | 2551 | 189.16 |
Shichang Liu | 4 | 1 | 0.37 |
S.A. Coleman | 5 | 6 | 1.61 |
Dermot Kerr | 6 | 50 | 13.84 |