关键词: 显著性检测, 全卷积网络, 先验信息引导, 循环卷积优化

Abstract:

In view of the lack of using prior features and edge information in the current significance detection algorithm based on deep learning, it is difficult to detect the significant regions with strong robustness in complex scenes, a method for significance detection of total convolutional neural network based on edge features and prior information is proposed. Firstly, the algorithm uses three kinds of frequently used prior knowledge combined with edge information to form a priori map, and the priori map features and depth features are effectively merged through the attention mechanism. Finally, the circular convolution-feedback optimization strategy is proposed to improve the significance area iteratively, so as to produce a more reliable prediction of the final significance graph. In the end, the reliability of the algorithm is proved by experimental qualitative and quantitative analysis.

Key words: saliency detection, full convolutional network, priori information guidance, circular convolution-feedback optimization