Significance detection based on deep convolutional neural network
Chenggang Ao, Jing Zhang, Yi Liu, Shuyun Wang, Weixin Zhang, Kai Zhang · 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) · 2022
Aiming at the limitations of the existing saliency detection algorithms in defining saliency features artificially and the poor robustness of the algorithm, a saliency detection algorithm based on deep convolutional neural network was proposed. Firstly, the VGG-16 model used for image classification is modified to make it applicable to significance detection. Then, the network is trained with super-pixel segmentation samples and the initial saliency graph is predicted. Finally, on the basis of preliminary significant image to join the regional contrast, increases significantly the difference of target and background region, further improve the quality of significant image. Compared with the existing algorithms, the proposed algorithm avoids the uncertainty of manually defining significant features, and has stronger robustness and generalization. The effectiveness of the proposed algorithm is demonstrated from two aspects of subjective qualitative observation and objective quantitative detection.