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Andreas Mueller
85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
"""
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====================================================
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Comparison of segmentation and superpixel algorithms
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====================================================
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This example compares three popular low-level image segmentation methods. As
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it is difficult do obtain good segmentations, and the definition of "good"
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often depends on the application, these methods are usually used for optaining
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an oversegmentation, also known as superpixels. These superpixels then serve as
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the level of operation for more sophisticated algorithms such as CRFs.
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Felzenszwalb's efficient graph based segmentation
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-------------------------------------------------
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This fast 2d image segmentation algorithm, proposed in [1]_ is popular in the
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computer vision community.
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The algorithm has a single ``scale`` parameter that influences the segment
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size. The actual size and number of segments can vary greatly, depending on
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local contrast.
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.. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and
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Huttenlocher, D.P. International Journal of Computer Vision, 2004
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Quickshift image segmentation
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-----------------------------
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Quickshift is a relatively recent 2d image segmentation algorithm, based on an
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approximation of kernelized mean-shift. Therefore it belongs to the family
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of local mode-seeking algorithms and is applied to the color+coordinate space,
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see [2]_.
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One of the benefits of quickshift is that it actually computes a
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hierarchical segmentation on multiple scales simultaneously.
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Quickshift has two parameters, one controlling the scale of the local
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density approximation, the other selecting a level in the hierarchical
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segmentation that is produced.
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.. [2] Quick shift and kernel methods for mode seeking,
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Vedaldi, A. and Soatto, S.
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European Conference on Computer Vision, 2008
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SLIC - K-Means based image segmentation
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---------------------------------------
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This algorithm simply performs K-kmeans in the 5d color-coordinate space and is
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therefore closely related to quickshift. As the clustering method is simpler,
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it is very efficient. It is essential for this algorithm to work in Lab color
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space to obtain good results. The algorithm quickly gained momentum and is now
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widely used. See [3] for details.
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.. [3] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,
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Pascal Fua, and Sabine Suesstrunk, SLIC Superpixels Compared to
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State-of-the-art Superpixel Methods, TPAMI, May 2012.
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"""
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print __doc__
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import matplotlib.pyplot as plt
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import numpy as np
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from skimage.data import lena
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from skimage.segmentation import felzenszwalb_segmentation, \
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visualize_boundaries, slic, quickshift
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from skimage.util import img_as_float
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img = img_as_float(lena()[::2, ::2])
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segments_fz = felzenszwalb_segmentation(img, scale=100, sigma=0.5, min_size=50)
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segments_slic = slic(img, ratio=10, n_segments=250, sigma=1)
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segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
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print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
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print("Slic number of segments: %d" % len(np.unique(segments_slic)))
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print("Quickshift number of segments: %d" % len(np.unique(segments_quick)))
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fig, ax = plt.subplots(1, 3)
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ax[0].imshow(visualize_boundaries(img, segments_fz))
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ax[1].imshow(visualize_boundaries(img, segments_slic))
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ax[2].imshow(visualize_boundaries(img, segments_quick))
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for a in ax:
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a.set_xticks(())
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a.set_yticks(())
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plt.show()
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