Image Saliency Detection via Two-Stream Feature Fusion and Adversarial Learning

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 strength ened 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,PASCALS,DUT-OMRON and DUTS-test show that SaTSAL algorithm is superior to most of other ten saliency detection methods based on deep learning.