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I also thought about augmenting this algorithm with the semantic information output of a neural network, but couldn't come up with a good way to generate traini

I also thought about augmenting this algorithm with the semantic information output of a neural network, but couldn't come up with a good way to generate training data for semantic segmentation which wouldn't distort objects. For example, if you consider an image of a roof, the roof pixels will all be semantically similar, but if you remove any of them just based on that, the regular structure will be distorted. Do you have an idea how to solve this?

rudi-c 9y ago
There's one paper I found that explores this approach: https://arxiv.org/pdf/1708.02731.pdf https://arxiv.org/pdf/1708.02731.pdf I know little about deep learning though, so I can't really comment on it. For situations like the roof problem you describe, I think PatchMatch is more likely to work out well. The downside is that it's considerably harder to implement correctly than seam carving. http://gfx.cs.princeton.edu/pubs/Barnes_2009_PAR/patchmatch.pdf http://gfx.cs.princeton.edu/pubs/Barnes_2009_PAR/patchmatch....