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MISC some typos in Example, titles set.

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Andreas Mueller
2012-08-05 20:25:26 +01:00
parent 8098718036
commit 1db4ebcecb
1 changed files with 20 additions and 14 deletions
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doc/examples/plot_segmentations.py
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@@ -4,10 +4,10 @@ Comparison of segmentation and superpixel algorithms
====================================================
This example compares three popular low-level image segmentation methods. As
it is difficult do obtain good segmentations, and the definition of "good"
often depends on the application, these methods are usually used for optaining
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
the level of operation for more sophisticated algorithms such as CRFs.
a basis for more sophisticated algorithms such as CRFs.
Felzenszwalb's efficient graph based segmentation
@@ -26,16 +26,17 @@ 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 [2]_.
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. see [2]_.
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.
Quickshift has three parameters: ``sigma`` controls the scale of the local
density approximation, ``max_dist`` other selecting 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.
@@ -44,11 +45,13 @@ segmentation that is produced.
SLIC - K-Means based image segmentation
---------------------------------------
This algorithm simply performs K-kmeans in the 5d color-coordinate space 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.
This algorithm simply performs K-kmeans 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
@@ -76,8 +79,11 @@ print("Quickshift number of segments: %d" % len(np.unique(segments_quick)))
fig, ax = plt.subplots(1, 3)
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(())