old_contents: """
  ==============
  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, Aug 2000
  """
  from skimage import graph, data, io, segmentation, color
  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()
  
new_contents: """
  ==============
  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.
  """
  from skimage import graph, data, io, segmentation, color
  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()
  
subject: change ref format in example