Global Image Sentiment Transfer
- Resource Type
- Conference
- Authors
- An, Jie; Chen, Tianlang; Zhang, Songyang; Luo, Jiebo
- Source
- 2020 25th International Conference on Pattern Recognition (ICPR) Pattern Recognition (ICPR), 2020 25th International Conference on. :6267-6274 Jan, 2021
- Subject
- Computing and Processing
Signal Processing and Analysis
Computer vision
Image retrieval
Computer architecture
Feature extraction
Loss measurement
Pattern recognition
Indexes
- Language
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.