diff --git a/doc/examples/plot_felzenszwalb_segmentation.py b/doc/examples/plot_felzenszwalb_segmentation.py
index 0b8a2e8c..27581fb1 100644
--- a/doc/examples/plot_felzenszwalb_segmentation.py
+++ b/doc/examples/plot_felzenszwalb_segmentation.py
@@ -1,3 +1,21 @@
+"""
+=================================================
+Felzenszwalb's efficient graph based segmentation
+=================================================
+
+This fast 2d image segmentation algorithm, proposed in [1]_ is popular in the
+computer vision community. It is often used to extract "superpixels", small
+homogeneous image regions, which build the basis for further processing.
+
+The algorithm has a single ``scale`` parameter that influences the segment
+size. The actual size and number of segments can vary greatly, depending on
+local contrast.
+
+.. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and
+       Huttenlocher, D.P.  International Journal of Computer Vision, 2004
+"""
+print __doc__
+
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -23,5 +41,5 @@ colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
 counts = np.bincount(segments.ravel())
 colors = np.vstack(colors) / counts
 plt.imshow(colors.T[segments], interpolation='nearest')
+print("number of segments: %d" % len(np.unique(segments)))
 plt.show()
-print("num segments: %d" % len(np.unique(segments)))
diff --git a/doc/examples/plot_quickshift.py b/doc/examples/plot_quickshift.py
index b21c56e8..29f44899 100644
--- a/doc/examples/plot_quickshift.py
+++ b/doc/examples/plot_quickshift.py
@@ -1,3 +1,26 @@
+"""
+=============================
+Quickshift image segmentation
+=============================
+
+Quickshift is a relatively recent 2d image segmentation algorithm, based on an
+approximation of kernelized mean-shift. Therefore it belongs to the family
+of local mode-seeking algorithms and is applied to the color+coordinate space,
+see [1]_ It is often used to extract "superpixels", small homogeneous image
+regions, which build the basis for further processing.
+
+One of the benefits of quickshift is that it actually computes a
+hierarchical segmentation on multiple scales simultaneously.
+
+Quickshift has two parameters, one controlling the scale of the local
+density approximation, the other selecting a level in the hierarchical
+segmentation that is produced.
+
+.. [1] Quick shift and kernel methods for mode seeking, Vedaldi, A. and Soatto, S.
+       European Conference on Computer Vision, 2008
+"""
+print __doc__
+
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -5,11 +28,8 @@ from skimage.data import lena
 from skimage.segmentation import quickshift
 from skimage.util import img_as_float
 
-from IPython.core.debugger import Tracer
-tracer = Tracer()
 
-
-img = img_as_float(lena())
+img = img_as_float(lena())[::2, ::2, :].copy("C")
 segments = quickshift(img, sigma=5, tau=20)
 segments = np.unique(segments, return_inverse=True)[1].reshape(img.shape[:2])
 
@@ -27,5 +47,5 @@ colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
 counts = np.bincount(segments.ravel())
 colors = np.vstack(colors) / counts
 plt.imshow(colors.T[segments], interpolation='nearest')
+print("number of segments: %d" % len(np.unique(segments)))
 plt.show()
-print("num segments: %d" % len(np.unique(segments)))