论文标题
全球图像情绪转移
Global Image Sentiment Transfer
论文作者
论文摘要
传递图像的情感是计算机视觉领域的一个未开发的研究主题。这项工作提出了一个新颖的框架，该框架由参考图像检索步骤和全局情感转移步骤，根据给定的情感标签转移图像的观点。提出的图像检索算法基于SSIM索引。根据所提出的算法检索到的参考图像与基于感知损失的算法更相关。因此，可以导致更好的图像情感转移结果。此外，我们提出了一个全球情感转移步骤，该步骤采用了基于Densenet121体系结构产生的特征图的迭代传输图像的优化算法。提出的情感转移算法可以传递图像的情感，同时确保输入图像的内容结构完整。定性和定量实验表明，拟议的情感转移框架优于现有的艺术和逼真的风格转移算法，以提供可靠的情感转移结果，并具有丰富的细节。
Transferring the sentiment of an image is an unexplored research topic in the area of computer vision. This work proposes a novel framework consisting of a reference image retrieval step and a global sentiment transfer step to transfer sentiments of images 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 against the algorithm based on the perceptual loss. Therefore 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 sentiment of images based on feature maps produced by the Densenet121 architecture. The proposed sentiment transfer algorithm can transfer the sentiment of images while ensuring the content structure of the input image intact. The qualitative and quantitative experiments demonstrate that the proposed sentiment transfer framework outperforms existing artistic and photorealistic style transfer algorithms in making reliable sentiment transfer results with rich and exact details.
