Co-saliency Detection Based on Convolutional Neural Network and Global Optimization

Zemin WU; Jun WANG; Lei HU; Chang TIAN; Mingyong ZENG; Lin DU

doi:10.11999/JEIT180241

Volume 40 Issue 12

Nov. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(12): 2896-2904

Zemin WU, Jun WANG, Lei HU, Chang TIAN, Mingyong ZENG, Lin DU. Co-saliency Detection Based on Convolutional Neural Network and Global Optimization[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2896-2904. doi: 10.11999/JEIT180241

Citation:

Zemin WU, Jun WANG, Lei HU, Chang TIAN, Mingyong ZENG, Lin DU. Co-saliency Detection Based on Convolutional Neural Network and Global Optimization[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2896-2904. doi: 10.11999/JEIT180241

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Co-saliency Detection Based on Convolutional Neural Network and Global Optimization

doi: 10.11999/JEIT180241

College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China

Received Date: 2018-03-16
Rev Recd Date: 2018-08-22

Available Online: 2018-08-31

Publish Date: 2018-12-01

Abstract

Abstract

To solve the problems in current co-saliency detection algorithms, a novel co-saliency detection algorithm is proposed which applies fully convolution neural network and global optimization model. First, a fully convolution saliency detection network is built based on VGG16Net. The network can simulate the human visual attention mechanism and extract the saliency region in an image from the semantic level. Second, based on the traditional saliency optimization model, the global co-saliency optimization model is constructed, which realizes the transmission and sharing of the current superpixel saliency value in inter-images and intra-image through superpixel matching, making the final saliency map has better co-saliency value. Third, the inter-image saliency value propagation constraint parameter is innovatively introduced to overcome the disadvantages of superpixel mismatching. Experimental results on public test datasets show that the proposed algorithm is superior over current state-of-the-art methods in terms of detection accuracy and detection efficiency, and has strong robustness.
- Co-saliency,
- Deep Learning,
- Convolutional Neural Network,
- Global Optimization

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References

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