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Image Style Transfer Algorithm Based on Semantic Segmentation
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DOI：10.1109/ACCESS.2021.3054969.png)
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摘要
En 中文
Most of the existing image style transfer algorithms transfer the whole image style as a whole. Style feature is a set of correlation matrix based on style image, namely Gram matrix. Each matrix is a global description of the style image. This kind of methods can perform well in the insensitive semantic scenes (such as the style transfer between landscape photos), but in the sensitive semantic scenes (such as the style transfer between portrait photos), the problem of semantic mismatch will be highlighted, such as transferring the background texture of the style image to the foreground of the target image. Although the existing research takes the manually annotated semantic image as an input of the algorithm, and then guides the style transfer based on the semantic information, and finally achieves good results in the style transfer between portraits. But there are still two problems: first, semantic images need to be manually annotated, which costs human resources. In practical applications, large-scale image style transfer is often needed. Second, the details of the synthesized image are fuzzy, and the definition is not enough. We propose an image style transfer algorithm based on semantic segmentation to resolve semantic mismatching in image style transfer. Our algorithm extracts the semantic information of style image and content image automatically through a semantic segmentation network and uses the semantic information to guide the style transfer. Our algorithm builds a semantic segmentation network based on mask R-CNN, introduces semantic information, and then makes style transfer on the patch level, realizes the style transfer between similar objects (consistent semantic information). Experiments on Celeba and Wikiart show that our method could automatically extract the semantic information of style image and content image. Compared with the state-of-art approaches in this field, our method can effectively avoid semantic mismatch in the process of image style transfer. That is, it can maintain semantic consistency in the process of style transfer.
Keyword:
Semantics
Feature extraction
Image segmentation
Data mining
Optimization
Kernel
Training
Image style transfer
semantic segmentation
semantic mismatching
feature extraction
fine semantic guidance
mask R-CNN
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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