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/plot_ncut_1.png

../../_images/plot_ncut_2.png

from skimage import data, io, 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')

plt.figure()
io.imshow(out1)
plt.figure()
io.imshow(out2)
io.show()

Python source code: download (generated using skimage 0.12.3)

IPython Notebook: download (generated using skimage 0.12.3)

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