Normalized Cut

This example constructs a Region Adjacency Graph (RAG) and recursively performs a Normalized Cut on it.

References

[1]Shi, J.; Malik, J., “Normalized cuts and image segmentation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000.
../../_images/sphx_glr_plot_ncut_001.png
from skimage import data, segmentation, color
from skimage.future import graph
from matplotlib import pyplot as plt


img = data.coffee()

labels1 = segmentation.slic(img, compactness=30, n_segments=400)
out1 = color.label2rgb(labels1, img, kind='avg')

g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg')

fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8))

ax[0].imshow(out1)
ax[1].imshow(out2)

for a in ax:
    a.axis('off')

plt.tight_layout()

Total running time of the script: ( 0 minutes 23.593 seconds)

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