Image Saliency Detection via Two-Stream Feature Fusion and Adversarial Learning
Yihan Zhang, Zhaohui Zhang, Lina Huo, Bin Xie, Xiuqing Wang · Journal of Computer-Aided Design & Computer Graphics · 2021
To achieve meaningful combination of low-level features and semantic information of salient regions or targets, and to obtain saliency detection results with more complete structure and clearer boundary, an algorithm of color image saliency detection via two-stream feature fusion and adversarial learning (SaTSAL) is proposed. Firstly, different levels of image features are extracted from bottom to top by means of a two-stream heterogeneous backbone network based on VGG-16 and Res2Net-50. Secondly, in each stream, different feature maps from the same level are fetched into one convolution tower module to enrich intra-level multi-scale information. Thirdly, a predicted saliency map is generated by top-down laterally fusing of cross-stream feature maps level by level, so as to effectively make full use of high-level semantic features and low-level image features. Finally, under the mainframe of conditional generative adversarial networks (CGAN), a higher structural similarity between detected results and salient objects can be strengthened by adversarial learning. By taking P-R curve, F-measure, mean absolute error and S-measure as evaluation indexes, comparative experiments performed on four public datasets including ECSSD, PASCAL- S, DUT-OMRON and DUTS-test show that SaTSAL algorithm is superior to most of other ten saliency detection methods based on deep learning.