3 ms·

yWorks employee, developer and one of the authors of that blog post (and the described code) here. Happy to answer any questions regarding graph drawing or reco

by ygra 7y ago

yWorks employee, developer and one of the authors of that blog post (and the described code) here. Happy to answer any questions regarding graph drawing or recognition.

burning_hamster 7y ago
Would you guys mind open sourcing your data set, including the cases where you currently fail (presumably graphs with edge crossings -- I couldn't help but notice that all examples are planar graphs)? I think I may have some ideas how to tackle some of the unresolved issues you state at the end of the blog post.
- ygra 7y ago
  Yeah, we intentionally omitted edge crossings. There's prior research in dealing with them and those approaches work well (and are clever), so we thought it wouldn't be terribly useful for us to re-invent that part. The papers are linked in the blog post near the end where we compare our approach with previous attempts. We've then concentrated on different segmentation strategies (the other approaches started with a sensible binarization of the image, which precludes color-based segmentation), as well as getting visual characteristics right, such as shape and color. The algorithms still don't handle cases well where features are much larger than we expect (e.g. photos are very different than screenshots). That'd be certainly an area of improvement. Our data set is ... basically most of what the screenshots in the article show (one of them opens an album with more images). We didn't have time for a thorough testing of thousands of different graphs. That's something we'd certainly have to do if we'd want to publish anything ;-)
dreamcompiler 7y ago
I would think the Hough transform would be an important tool in your image recognition box but didn't see any mention of it.
- ygra 7y ago
  I thought about using it, but since it returns only straight lines in the image it's probably not very useful for curved edges and for node shapes there are probably also better ways. For edges the skeleton has been working really well. It might still be useful for detecting whether edges are orthogonal and to better detect bends (right now, orthogonal edges always have a little curve at the corners, due to how skeletonization works).

by ygra 7y ago

yWorks employee, developer and one of the authors of that blog post (and the described code) here. Happy to answer any questions regarding graph drawing or recognition.

burning_hamster 7y ago

Would you guys mind open sourcing your data set, including the cases where you currently fail (presumably graphs with edge crossings -- I couldn't help but notice that all examples are planar graphs)? I think I may have some ideas how to tackle some of the unresolved issues you state at the end of the blog post.

ygra 7y ago
Yeah, we intentionally omitted edge crossings. There's prior research in dealing with them and those approaches work well (and are clever), so we thought it wouldn't be terribly useful for us to re-invent that part. The papers are linked in the blog post near the end where we compare our approach with previous attempts. We've then concentrated on different segmentation strategies (the other approaches started with a sensible binarization of the image, which precludes color-based segmentation), as well as getting visual characteristics right, such as shape and color. The algorithms still don't handle cases well where features are much larger than we expect (e.g. photos are very different than screenshots). That'd be certainly an area of improvement. Our data set is ... basically most of what the screenshots in the article show (one of them opens an album with more images). We didn't have time for a thorough testing of thousands of different graphs. That's something we'd certainly have to do if we'd want to publish anything ;-)

dreamcompiler 7y ago

I would think the Hough transform would be an important tool in your image recognition box but didn't see any mention of it.

ygra 7y ago
I thought about using it, but since it returns only straight lines in the image it's probably not very useful for curved edges and for node shapes there are probably also better ways. For edges the skeleton has been working really well. It might still be useful for detecting whether edges are orthogonal and to better detect bends (right now, orthogonal edges always have a little curve at the corners, due to how skeletonization works).