diff --git a/bento.info b/bento.info
index 227e51e8..d0891272 100644
--- a/bento.info
+++ b/bento.info
@@ -154,6 +154,9 @@ Library:
     Extension: skimage.feature._hessian_det_appx
         Sources:
             skimage/exposure/_hessian_det_appx.pyx
+    Extension: skimage.graph._ncut_cy
+        Sources:
+            skimage/graph/_ncut_cy.pyx
 
 Executable: skivi
     Module: skimage.scripts.skivi
diff --git a/doc/examples/plot_ncut.py b/doc/examples/plot_ncut.py
new file mode 100644
index 00000000..6b8a62ab
--- /dev/null
+++ b/doc/examples/plot_ncut.py
@@ -0,0 +1,32 @@
+"""
+==============
+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()
diff --git a/skimage/graph/__init__.py b/skimage/graph/__init__.py
index 68c1206c..aca73bc4 100644
--- a/skimage/graph/__init__.py
+++ b/skimage/graph/__init__.py
@@ -1,7 +1,8 @@
 from .spath import shortest_path
 from .mcp import MCP, MCP_Geometric, MCP_Connect, MCP_Flexible, route_through_array
 from .rag import rag_mean_color, RAG
-from .graph_cut import cut_threshold
+from .graph_cut import cut_threshold, cut_normalized
+ncut = cut_normalized
 
 __all__ = ['shortest_path',
            'MCP',
@@ -11,4 +12,6 @@ __all__ = ['shortest_path',
            'route_through_array',
            'rag_mean_color',
            'cut_threshold',
+           'cut_normalized',
+           'ncut',
            'RAG']
diff --git a/skimage/graph/_ncut.py b/skimage/graph/_ncut.py
new file mode 100644
index 00000000..2735bbd2
--- /dev/null
+++ b/skimage/graph/_ncut.py
@@ -0,0 +1,85 @@
+try:
+    import networkx as nx
+except ImportError:
+    import warnings
+    warnings.warn('RAGs require networkx')
+import numpy as np
+from scipy import sparse
+from . import _ncut_cy
+
+
+def DW_matrices(graph):
+    """Returns the diagonal and weight matrices of a graph.
+
+    Parameters
+    ----------
+    graph : RAG
+        A Region Adjacency Graph.
+
+    Returns
+    -------
+    D : csc_matrix
+        The diagonal matrix of the graph. ``D[i, i]`` is the sum of weights of
+        all edges incident on `i`. All other enteries are `0`.
+    W : csc_matrix
+        The weight matrix of the graph. ``W[i, j]`` is the weight of the edge
+        joining `i` to `j`.
+    """
+    # sparse.eighsh is most efficient with CSC-formatted input
+    W = nx.to_scipy_sparse_matrix(graph, format='csc')
+    entries = W.sum(axis=0)
+    D = sparse.dia_matrix((entries, 0), shape=W.shape).tocsc()
+    return D, W
+
+
+def ncut_cost(cut, D, W):
+    """Returns the N-cut cost of a bi-partition of a graph.
+
+    Parameters
+    ----------
+    cut : ndarray
+        The mask for the nodes in the graph. Nodes corressponding to a `True`
+        value are in one set.
+    D : csc_matrix
+        The diagonal matrix of the graph.
+    W : csc_matrix
+        The weight matrix of the graph.
+
+    Returns
+    -------
+    cost : float
+        The cost of performing the N-cut.
+
+    References
+    ----------
+    .. [1] Normalized Cuts and Image Segmentation, Jianbo Shi and
+           Jitendra Malik, IEEE Transactions on Pattern Analysis and Machine
+           Intelligence, Page 889, Equation 2.
+    """
+    cut = np.array(cut)
+    cut_cost = _ncut_cy.cut_cost(cut, W)
+
+    # D has elements only along the diagonal, one per node, so we can directly
+    # index the data attribute with cut.
+    assoc_a = D.data[cut].sum()
+    assoc_b = D.data[~cut].sum()
+
+    return (cut_cost / assoc_a) + (cut_cost / assoc_b)
+
+
+def normalize(a):
+    """Normalize values in an array between `0` and `1`.
+
+    Parameters
+    ----------
+    a : ndarray
+        The array to be normalized.
+
+    Returns
+    -------
+    out : ndarray
+        The normalized array.
+    """
+    mi = a.min()
+    mx = a.max()
+    return (a - mi) / (mx - mi)
diff --git a/skimage/graph/_ncut_cy.pyx b/skimage/graph/_ncut_cy.pyx
new file mode 100644
index 00000000..14f3a94a
--- /dev/null
+++ b/skimage/graph/_ncut_cy.pyx
@@ -0,0 +1,78 @@
+# cython: cdivision=True
+# cython: boundscheck=False
+# cython: nonecheck=False
+# cython: wraparound=False
+cimport numpy as cnp
+import numpy as np
+
+
+def argmin2(cnp.double_t[:] array):
+    """Return the index of the 2nd smallest value in an array.
+
+    Parameters
+    ----------
+    array : array
+        The array to process.
+
+    Returns
+    -------
+    min_idx2 : int
+        The index of the second smallest value.
+    """
+    cdef cnp.float64_t min1 = np.inf
+    cdef cnp.float64_t min2 = np.inf
+    cdef Py_ssize_t min_idx1 = 0
+    cdef Py_ssize_t min_idx2 = 0
+    cdef Py_ssize_t i = 0
+    cdef Py_ssize_t n = array.shape[0]
+
+    for i in range(n):
+        x = array[i]
+        if x < min1:
+            min2 = min1
+            min_idx2 = min_idx1
+            min1 = x
+            min_idx1 = i
+        elif x > min1 and x < min2:
+            min2 = x
+            min_idx2 = i
+        i += 1
+
+    return min_idx2
+
+
+def cut_cost(cut, W):
+    """Return the total weight of crossing edges in a bi-partition.
+
+    Parameters
+    ----------
+    cut : array
+        A array of booleans. Elements set to `True` belong to one
+        set.
+    W : array
+        The weight matrix of the graph.
+
+    Returns
+    -------
+    cost : float
+        The total weight of crossing edges.
+    """
+    cdef cnp.ndarray[cnp.uint8_t, cast = True] cut_mask = np.array(cut)
+    cdef Py_ssize_t num_rows, num_cols
+    cdef cnp.int32_t row, col
+    cdef cnp.int32_t[:] indices = W.indices
+    cdef cnp.int32_t[:] indptr = W.indptr
+    cdef cnp.double_t[:] data = W.data.astype(np.double)
+    cdef cnp.int32_t row_index
+    cdef cnp.double_t cost = 0
+
+    num_rows = W.shape[0]
+    num_cols = W.shape[1]
+
+    for col in range(num_cols):
+        for row_index in range(indptr[col], indptr[col + 1]):
+            row = indices[row_index]
+            if cut_mask[row] != cut_mask[col]:
+                cost += data[row_index]
+
+    return cost * 0.5
diff --git a/skimage/graph/graph_cut.py b/skimage/graph/graph_cut.py
index d9fcfdef..3f973e22 100644
--- a/skimage/graph/graph_cut.py
+++ b/skimage/graph/graph_cut.py
@@ -2,11 +2,14 @@ try:
     import networkx as nx
 except ImportError:
     import warnings
-    warnings.warn('"cut_threshold" requires networkx')
+    warnings.warn('RAGs require networkx')
 import numpy as np
+from . import _ncut
+from . import _ncut_cy
+from scipy.sparse import linalg
 
 
-def cut_threshold(labels, rag, thresh):
+def cut_threshold(labels, rag, thresh, in_place=True):
     """Combine regions seperated by weight less than threshold.
 
     Given an image's labels and its RAG, output new labels by
@@ -22,6 +25,10 @@ def cut_threshold(labels, rag, thresh):
     thresh : float
         The threshold. Regions connected by edges with smaller weights are
         combined.
+    in_place : bool
+        If set, modifies `rag` in place. The function will remove the edges
+        with weights less that `thresh`. If set to `False` the function
+        makes a copy of `rag` before proceeding.
 
     Returns
     -------
@@ -43,6 +50,9 @@ def cut_threshold(labels, rag, thresh):
            http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5274
 
     """
+    if not in_place:
+        rag = rag.copy()
+
     # Because deleting edges while iterating through them produces an error.
     to_remove = [(x, y) for x, y, d in rag.edges_iter(data=True)
                  if d['weight'] >= thresh]
@@ -60,3 +70,206 @@ def cut_threshold(labels, rag, thresh):
                 map_array[label] = i
 
     return map_array[labels]
+
+
+def cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True):
+    """Perform Normalized Graph cut on the Region Adjacency Graph.
+
+    Given an image's labels and its similarity RAG, recursively perform
+    a 2-way normalized cut on it. All nodes belonging to a subgraph
+    that cannot be cut further are assigned a unique label in the
+    output.
+
+    Parameters
+    ----------
+    labels : ndarray
+        The array of labels.
+    rag : RAG
+        The region adjacency graph.
+    thresh : float
+        The threshold. A subgraph won't be further subdivided if the
+        value of the N-cut exceeds `thresh`.
+    num_cuts : int
+        The number or N-cuts to perform before determining the optimal one.
+    in_place : bool
+        If set, modifies `rag` in place. For each node `n` the function will
+        set a new attribute ``rag.node[n]['ncut label']``.
+
+    Returns
+    -------
+    out : ndarray
+        The new labeled array.
+
+    Examples
+    --------
+    >>> from skimage import data, graph, segmentation
+    >>> img = data.lena()
+    >>> labels = segmentation.slic(img, compactness=30, n_segments=400)
+    >>> rag = graph.rag_mean_color(img, labels, mode='similarity')
+    >>> new_labels = graph.cut_normalized(labels, rag)
+
+    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.
+
+    """
+    if not in_place:
+        rag = rag.copy()
+
+    _ncut_relabel(rag, thresh, num_cuts)
+
+    map_array = np.zeros(labels.max() + 1)
+    # Mapping from old labels to new
+    for n, d in rag.nodes_iter(data=True):
+        map_array[d['labels']] = d['ncut label']
+
+    return map_array[labels]
+
+
+def partition_by_cut(cut, rag):
+    """Compute resulting subgraphs from given bi-parition.
+
+    Parameters
+    ----------
+    cut : array
+        A array of booleans. Elements set to `True` belong to one
+        set.
+    rag : RAG
+        The Region Adjacency Graph.
+
+    Returns
+    -------
+    sub1, sub2 : RAG
+        The two resulting subgraphs from the bi-partition.
+    """
+    # `cut` is derived from `D` and `W` matrices, which also follow the
+    # ordering returned by `rag.nodes()` because we use
+    # nx.to_scipy_sparse_matrix.
+
+    # Example
+    # rag.nodes() = [3, 7, 9, 13]
+    # cut = [True, False, True, False]
+    # nodes1 = [3, 9]
+    # nodes2 = [7, 10]
+
+    nodes1 = [n for i, n in enumerate(rag.nodes()) if cut[i]]
+    nodes2 = [n for i, n in enumerate(rag.nodes()) if not cut[i]]
+
+    sub1 = rag.subgraph(nodes1)
+    sub2 = rag.subgraph(nodes2)
+
+    return sub1, sub2
+
+
+def get_min_ncut(ev, d, w, num_cuts):
+    """Threshold an eigenvector evenly, to determine minimum ncut.
+
+    Parameters
+    ----------
+    ev : array
+        The eigenvector to threshold.
+    d : ndarray
+        The diagonal matrix of the graph.
+    w : ndarray
+        The weight matrix of the graph.
+    num_cuts : int
+        The number of evenly spaced thresholds to check for.
+
+    Returns
+    -------
+    mask : array
+        The array of booleans which denotes the bi-partition.
+    mcut : float
+        The value of the minimum ncut.
+    """
+    mcut = np.inf
+
+    # Refer Shi & Malik 2001, Section 3.1.3, Page 892
+    # Perform evenly spaced n-cuts and determine the optimal one.
+    for t in np.linspace(0, 1, num_cuts, endpoint=False):
+        mask = ev > t
+        cost = _ncut.ncut_cost(mask, d, w)
+        if cost < mcut:
+            min_mask = mask
+            mcut = cost
+
+    return min_mask, mcut
+
+
+def _label_all(rag, attr_name):
+    """Assign a unique integer to the given attribute in the RAG.
+
+    This function assumes that all labels in `rag` are unique. It
+    picks up a random label from them and assigns it to the `attr_name`
+    attribute of all the nodes.
+
+    rag : RAG
+        The Region Adjacency Graph.
+    attr_name : string
+        The attribute to which a unique integer is assigned.
+    """
+    node = rag.nodes()[0]
+    new_label = rag.node[node]['labels'][0]
+    for n, d in rag.nodes_iter(data=True):
+        d[attr_name] = new_label
+
+
+def _ncut_relabel(rag, thresh, num_cuts):
+    """Perform Normalized Graph cut on the Region Adjacency Graph.
+
+    Recursively partition the graph into 2, until further subdivision
+    yields a cut greather than `thresh` or such a cut cannot be computed.
+    For such a subgraph, indices to labels of all its nodes map to a single
+    unique value.
+
+    Parameters
+    ----------
+    labels : ndarray
+        The array of labels.
+    rag : RAG
+        The region adjacency graph.
+    thresh : float
+        The threshold. A subgraph won't be further subdivided if the
+        value of the N-cut exceeds `thresh`.
+    num_cuts : int
+        The number or N-cuts to perform before determining the optimal one.
+    map_array : array
+        The array which maps old labels to new ones. This is modified inside
+        the function.
+    """
+    d, w = _ncut.DW_matrices(rag)
+    m = w.shape[0]
+
+    if m > 2:
+        d2 = d.copy()
+        # Since d is diagonal, we can directly operate on its data
+        # the inverse of the square root
+        d2.data = np.reciprocal(np.sqrt(d2.data, out=d2.data), out=d2.data)
+
+        # Refer Shi & Malik 2001, Equation 7, Page 891
+        vals, vectors = linalg.eigsh(d2 * (d - w) * d2, which='SM',
+                                     k=min(100, m - 2))
+
+        # Pick second smallest eigenvector.
+        # Refer Shi & Malik 2001, Section 3.2.3, Page 893
+        vals, vectors = np.real(vals), np.real(vectors)
+        index2 = _ncut_cy.argmin2(vals)
+        ev = _ncut.normalize(vectors[:, index2])
+
+        cut_mask, mcut = get_min_ncut(ev, d, w, num_cuts)
+        if (mcut < thresh):
+            # Sub divide and perform N-cut again
+            # Refer Shi & Malik 2001, Section 3.2.5, Page 893
+            sub1, sub2 = partition_by_cut(cut_mask, rag)
+
+            _ncut_relabel(sub1, thresh, num_cuts)
+            _ncut_relabel(sub2, thresh, num_cuts)
+            return
+
+    # The N-cut wasn't small enough, or could not be computed.
+    # The remaining graph is a region.
+    # Assign `ncut label` by picking any label from the existing nodes, since
+    # `labels` are unique, `new_label` is also unique.
+    _label_all(rag, 'ncut label')
diff --git a/skimage/graph/rag.py b/skimage/graph/rag.py
index 7c1b7f07..5ef9a83b 100644
--- a/skimage/graph/rag.py
+++ b/skimage/graph/rag.py
@@ -2,6 +2,7 @@ try:
     import networkx as nx
 except ImportError:
     msg = "Graph functions require networkx, which is not installed"
+
     class nx:
         class Graph:
             def __init__(self, *args, **kwargs):
@@ -11,6 +12,7 @@ except ImportError:
 import numpy as np
 from scipy.ndimage import filters
 from scipy import ndimage as nd
+import math
 
 
 def min_weight(graph, src, dst, n):
@@ -118,14 +120,15 @@ def _add_edge_filter(values, graph):
     return 0
 
 
-def rag_mean_color(image, labels, connectivity=2):
+def rag_mean_color(image, labels, connectivity=2, mode='distance',
+                   sigma=255.0):
     """Compute the Region Adjacency Graph using mean colors.
 
     Given an image and its initial segmentation, this method constructs the
     corresponsing Region Adjacency Graph (RAG). Each node in the RAG
     represents a set of pixels within `image` with the same label in `labels`.
-    The weight between two adjacent regions is the difference in their mean
-    color.
+    The weight between two adjacent regions represents how similar or
+    dissimilar two regions are depending on the `mode` parameter.
 
     Parameters
     ----------
@@ -140,6 +143,23 @@ def rag_mean_color(image, labels, connectivity=2):
         are considered adjacent. It can range from 1 to `labels.ndim`. Its
         behavior is the same as `connectivity` parameter in
         `scipy.ndimage.filters.generate_binary_structure`.
+    mode : {'distance', 'similarity'}, optional
+        The strategy to assign edge weights.
+
+            'distance' : The weight between two adjacent regions is the
+            :math:`|c_1 - c_2|`, where :math:`c_1` and :math:`c_2` are the mean
+            colors of the two regions. It represents the Euclidean distance in
+            their average color.
+
+            'similarity' : The weight between two adjacent is
+            :math:`e^{-d^2/sigma}` where :math:`d=|c_1 - c_2|`, where
+            :math:`c_1` and :math:`c_2` are the mean colors of the two regions.
+            It represents how similar two regions are.
+    sigma : float, optional
+        Used for computation when `mode` is "similarity". It governs how
+        close to each other two colors should be, for their corresponding edge
+        weight to be significant. A very large value of `sigma` could make
+        any two colors behave as though they were similar.
 
     Returns
     -------
@@ -205,8 +225,14 @@ def rag_mean_color(image, labels, connectivity=2):
         graph.node[n]['mean color'] = (graph.node[n]['total color'] /
                                        graph.node[n]['pixel count'])
 
-    for x, y in graph.edges_iter():
+    for x, y, d in graph.edges_iter(data=True):
         diff = graph.node[x]['mean color'] - graph.node[y]['mean color']
-        graph[x][y]['weight'] = np.linalg.norm(diff)
+        diff = np.linalg.norm(diff)
+        if mode == 'similarity':
+            d['weight'] = math.e ** (-(diff ** 2) / sigma)
+        elif mode == 'distance':
+            d['weight'] = diff
+        else:
+            raise ValueError("The mode '%s' is not recognised" % mode)
 
     return graph
diff --git a/skimage/graph/setup.py b/skimage/graph/setup.py
index 463d2739..edc7b653 100644
--- a/skimage/graph/setup.py
+++ b/skimage/graph/setup.py
@@ -17,6 +17,7 @@ def configuration(parent_package='', top_path=None):
     cython(['_spath.pyx'], working_path=base_path)
     cython(['_mcp.pyx'], working_path=base_path)
     cython(['heap.pyx'], working_path=base_path)
+    cython(['_ncut_cy.pyx'], working_path=base_path)
 
     config.add_extension('_spath', sources=['_spath.c'],
                          include_dirs=[get_numpy_include_dirs()])
@@ -24,7 +25,8 @@ def configuration(parent_package='', top_path=None):
                          include_dirs=[get_numpy_include_dirs()])
     config.add_extension('heap', sources=['heap.c'],
                          include_dirs=[get_numpy_include_dirs()])
-
+    config.add_extension('_ncut_cy', sources=['_ncut_cy.c'],
+                         include_dirs=[get_numpy_include_dirs()])
     return config
 
 if __name__ == '__main__':
diff --git a/skimage/graph/tests/test_rag.py b/skimage/graph/tests/test_rag.py
index 753bcb74..0e62aa1a 100644
--- a/skimage/graph/tests/test_rag.py
+++ b/skimage/graph/tests/test_rag.py
@@ -2,6 +2,8 @@ import numpy as np
 from skimage import graph
 from skimage._shared.version_requirements import is_installed
 from numpy.testing.decorators import skipif
+from skimage import segmentation
+from numpy import testing
 
 
 def max_edge(g, src, dst, n):
@@ -62,7 +64,45 @@ def test_threshold_cut():
     labels[50:, 50:] = 3
 
     rag = graph.rag_mean_color(img, labels)
-    new_labels = graph.cut_threshold(labels, rag, 10)
-
+    new_labels = graph.cut_threshold(labels, rag, 10, in_place=False)
     # Two labels
     assert new_labels.max() == 1
+
+    new_labels = graph.cut_threshold(labels, rag, 10)
+    # Two labels
+    assert new_labels.max() == 1
+
+
+@skipif(not is_installed('networkx'))
+def test_cut_normalized():
+
+    img = np.zeros((100, 100, 3), dtype='uint8')
+    img[:50, :50] = 255, 255, 255
+    img[:50, 50:] = 254, 254, 254
+    img[50:, :50] = 2, 2, 2
+    img[50:, 50:] = 1, 1, 1
+
+    labels = np.zeros((100, 100), dtype='uint8')
+    labels[:50, :50] = 0
+    labels[:50, 50:] = 1
+    labels[50:, :50] = 2
+    labels[50:, 50:] = 3
+
+    rag = graph.rag_mean_color(img, labels, mode='similarity')
+
+    new_labels = graph.cut_normalized(labels, rag, in_place=False)
+    new_labels, _, _ = segmentation.relabel_sequential(new_labels)
+    # Two labels
+    assert new_labels.max() == 1
+
+    new_labels = graph.cut_normalized(labels, rag)
+    new_labels, _, _ = segmentation.relabel_sequential(new_labels)
+    assert new_labels.max() == 1
+
+
+@skipif(not is_installed('networkx'))
+def test_rag_error():
+    img = np.zeros((10, 10, 3), dtype='uint8')
+    labels = np.zeros((10, 10), dtype='uint8')
+    testing.assert_raises(ValueError, graph.rag_mean_color, img, labels,
+                          2, 'non existant mode')