Not too long ago I was reading a bunch of segmentation papers and came across “tobogganing” for unsupervised hierarchical image segmentation/clustering. Tobogganing is similar to watersheds and starts by clustering pixels into neighboring pixels that have the lowest gradient magnitude. The technique is efficient and can be made hierarchical by introducing a measure of similarity between neighboring clusters (the similarity is based on average colors and variances). This gives rise to a hierarchy of segmentations.
The method was used in LiveWire-based segmentation techniques (i.e., intelligent scissors), and has also been used in its hierarchical form for interactive medical image segmentation. In the latter application, the
hierarchy allows for quick solutions by computing the solution on the coarsest levels, and only refining it to finer levels when necessary. Thus avoiding redundant computation far away from the segmentation boundary.
I was interested in this application of tobogganing, and had to refrain from trying to implement it as I had no real use for it. But I also thought that the method might be useful for creating interesting image effects, which would be useful in another side-project. So, with application in mind, I tried a quick implementation this past weekend.
I have several potential ways of using the segmentation, but one of them was to try and help identify image edges that were more salient than the ones you typically get out of an edge processing algorithm (maybe smoothing regions with some Laplacian regularizer would give neat looking shading).
Below are some illustrations of the hierarchy for some input images. Tobogganed regions have been replaced with their average color.
Tobogganing effects (bike image)
Les neil tobogganing segmentation
The rightmost image gives an idea of how image edges could be used; the image edges extracted from an edge operator have been modulated with those extracted from the coarse levels of the tobogganed regions. Click on image for full-resolution. They are pretty big.
Preliminary source code:
