Merge pull request #206 from amueller/felsenzwalb

ENH: MRG Segmentation algorithms.

doc/examples/plot_segmentations.py

@@ -0,0 +1,91 @@
+"""
+====================================================
+Comparison of segmentation and superpixel algorithms
+====================================================
+
+This example compares three popular low-level image segmentation methods.  As
+it is difficult to obtain good segmentations, and the definition of "good"
+often depends on the application, these methods are usually used for obtaining
+an oversegmentation, also known as superpixels. These superpixels then serve as
+a basis for more sophisticated algorithms such as CRFs.
+
+
+Felzenszwalb's efficient graph based segmentation
+-------------------------------------------------
+This fast 2D image segmentation algorithm, proposed in [1]_ is popular in the
+computer vision community.
+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
+
+
+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 5D space consisting of
+color information and image location [2]_.
+
+One of the benefits of quickshift is that it actually computes a
+hierarchical segmentation on multiple scales simultaneously.
+
+Quickshift has two main parameters: ``sigma`` controls the scale of the local
+density approximation, ``max_dist`` selects a level in the hierarchical
+segmentation that is produced. There is also a trade-off between distance in
+color-space and distance in image-space, given by ``ratio``.
+
+.. [2] Quick shift and kernel methods for mode seeking,
+       Vedaldi, A. and Soatto, S.
+       European Conference on Computer Vision, 2008
+
+
+SLIC - K-Means based image segmentation
+---------------------------------------
+This algorithm simply performs K-means in the 5d space of color information
+and image location and is therefore closely related to quickshift. As the
+clustering method is simpler, it is very efficient. It is essential for this
+algorithm to work in Lab color space to obtain good results.  The algorithm
+quickly gained momentum and is now widely used. See [3] for details.  The
+``ratio`` parameter trades off color-similarity and proximity, as in the case
+of Quickshift, while ``n_segments`` chooses the number of centers for kmeans.
+
+.. [3] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,
+    Pascal Fua, and Sabine Suesstrunk, SLIC Superpixels Compared to
+    State-of-the-art Superpixel Methods, TPAMI, May 2012.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from skimage.data import lena
+from skimage.segmentation import felzenszwalb, \
+    visualize_boundaries, slic, quickshift
+from skimage.util import img_as_float
+
+img = img_as_float(lena()[::2, ::2])
+segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
+segments_slic = slic(img, ratio=10, n_segments=250, sigma=1)
+segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
+
+print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
+print("Slic number of segments: %d" % len(np.unique(segments_slic)))
+print("Quickshift number of segments: %d" % len(np.unique(segments_quick)))
+
+fig, ax = plt.subplots(1, 3)
+fig.set_size_inches(8, 3, forward=True)
+plt.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
+
+ax[0].imshow(visualize_boundaries(img, segments_fz))
+ax[0].set_title("Felzenszwalbs's method")
+ax[1].imshow(visualize_boundaries(img, segments_slic))
+ax[1].set_title("SLIC")
+ax[2].imshow(visualize_boundaries(img, segments_quick))
+ax[2].set_title("Quickshift")
+for a in ax:
+    a.set_xticks(())
+    a.set_yticks(())
+plt.show()