Saliency detection with FCNN based on low-level feature optimization

Hao Zhang; Linhua Jiang; Xiaodong Chen

doi:10.1117/12.2514444

29 October 2018 Saliency detection with FCNN based on low-level feature optimization

Hao Zhang, Linhua Jiang, Xiaodong Chen

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360N (2018) https://doi.org/10.1117/12.2514444
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

In order to improve the accuracy of saliency target recognition in digital images, this paper proposes a saliency detection algorithm based on low-level feature optimization for full convolution neural networks. Firstly, a fully convolutional neural network is constructed and trained on the basis of the VGG-16 network, and the initial saliency map is obtained through the output of the full convolutional neural network. Then, the input image is super-pixel divided, and the super pixel is regarded as a vertex of a graph to compose. On the basis of the initial saliency map, the superpixel saliency division is performed. The selected initial seed points are selected based on the central prior, and the low-level eigenvalues such as the superpixel RGB eigenvalues are calculated, and the saliency region merging is performed to obtain the saliency optimization map based on the low-level feature optimization. Finally, the initial saliency map and the saliency optimization map are combined to obtain the final saliency map. The comparison experiments show that the proposed algorithm achieves the excellent precision compared with other algorithms, and illustrates the effectiveness of the algorithm.

Citation Download Citation

Hao Zhang, Linhua Jiang, and Xiaodong Chen "Saliency detection with FCNN based on low-level feature optimization", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360N (29 October 2018); https://doi.org/10.1117/12.2514444

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