Abstract
This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new method based on an adaptive patch partition that captures the style of the example image and preserves the structure of the source image. More precisely, this example-based partition predicts how well a source patch matches an example patch. Results on various images show that our method outperforms the most recent techniques.
Keywords
Computer sciencePartition (number theory)Artificial intelligenceImage (mathematics)Image textureComputer visionPattern recognition (psychology)Transfer (computing)Style (visual arts)Texture synthesisImage segmentationMathematics
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Publication Info
- Year
- 2016
- Type
- preprint
- Pages
- 553-561
- Citations
- 130
- Access
- Closed
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Cite This
Oriel Frigo,
N. Sabaté,
Julie Delon
et al.
(2016).
Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer.
, 553-561.
https://doi.org/10.1109/cvpr.2016.66
Identifiers
- DOI
- 10.1109/cvpr.2016.66