Revisiting graph-cut segmentation with SLIC and color histograms [w/Python]

As part of the computer vision class I’m teaching at SBU I asked students to implement a segmentation method based on SLIC superpixels. Here is my boilerplate implementation.
This follows the work I’ve done a very long time ago (2010) on the same subject.
For graph-cut I’ve used PyMaxflow: https://github.com/pmneila/PyMaxflow, which is very easily installed by just pip install PyMaxflow
The method is simple:

• Calculate SLIC superpixels (the SKImage implementation)
• Use markings to determine the foreground and background color histograms (from the superpixels under the markings)
• Setup a graph with a straightforward energy model: Smoothness term = K-L-Div between superpix histogram and neighbor superpix histogram, and Match term = inf if marked as BG or FG, or K-L-Div between SuperPix histogram and FG and BG.
• To find neighbors I’ve used Delaunay tessellation (from scipy.spatial), for simplicity. But a full neighbor finding could be implemented by looking at all the neighbors on the superpix’s boundary.
• Color histograms are 2D over H-S (from the HSV)

Result