Global Image Sentiment Transfer
Global Image Sentiment Transfer
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DOI：
10.1109/icpr48806.2021.9413221
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Jie An;Tianlang Chen;Songyang Zhang;Jiebo Luo
中科院分区：
文献类型：
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作者：
Jie An;Tianlang Chen;Songyang Zhang;Jiebo Luo
传递图像的情感是计算机视觉中一个尚未探索的研究课题。这项工作提出了一种新的框架，包括一个参考图像检索步骤和一个全球的情感转移步骤，根据一个给定的情感标签转移图像的情感。提出的图像检索算法是基于SSIM索引。与基于感知损失的算法相比，该算法检索到的参考图像具有更好的内容相关性。因此，它可以导致更好的图像情感传递结果。此外，我们提出了一个全局情感转移步骤，它采用了一种优化算法来迭代地转移图像情感的基础上产生的特征图的DenseNet121架构。所提出的情感转移算法可以在保持输入图像内容完整的同时转移图像情感。定性和定量的评估表明，所提出的情感转移框架优于现有的艺术和照片逼真的风格转移算法，产生令人满意的情感转移结果与精细和准确的细节。
Transferring the sentiment of an image is an unexplored research topic in computer vision. This work proposes a novel framework consisting of a reference image retrieval step and a global sentiment transfer step to transfer image sentiment according to a given sentiment tag. The proposed image retrieval algorithm is based on the SSIM index. The retrieved reference images by the proposed algorithm are more content-related than the algorithm based on the perceptual loss. Therefore, it can lead to a better image sentiment transfer result. In addition, we propose a global sentiment transfer step, which employs an optimization algorithm to iteratively transfer image sentiment based on the feature maps produced by the DenseNet121 architecture. The proposed sentiment transfer algorithm can transfer image sentiment while keeping the content of the input image intact. Both qualitative and quantitative evaluations demonstrate that the proposed sentiment transfer framework outperforms existing artistic and photo-realistic style transfer algorithms in producing satisfactory sentiment transfer results with fine and exact details.