SLIC Superpixels

“Superpixel segmentation algorithms can be very useful as a preprocessing step for computer vision applications like object class recognition and medical image segmentation. To be useful, such algorithms should output high quality superpixels. There are few superpixel algorithms that can offer this and scale up for practical applications that deal with images greater than 0.5 million pixels. We present a novel O(N) complexity superpixel segmentation algorithm that is simple to implement and outputs better quality superpixels for a very low computational and memory cost…”
Radhakrishna Achanta et al.

Preliminaries

import matplotlib.pyplot as plt
from skimage.segmentation import slic
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
from skimage import io

%pylab inline
%matplotlib inline 

Populating the interactive namespace from numpy and matplotlib

Input image

img = img_as_float(io.imread('/home/cobalt/Pictures/frances2.jpg'))
io.imshow(img);

_config.yml

Oversegmentation to produce the Superpixels

for segs in (10, 50, 100, 300, 500, 1000):
    segments = slic(img, n_segments = segs, sigma = 4)
    fig = plt.figure(figsize=(12,4))
    ax = fig.add_axes([0, 0, 1, 1])
    ax.imshow(mark_boundaries(img, segments)) 
plt.show()

_config.yml

_config.yml

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_config.yml

Comparison of segmentation and superpixel algorithms

http://scikit-image.org/docs/dev/auto_examples/plot_segmentations.html

from __future__ import print_function

import matplotlib.pyplot as plt
import numpy as np

from skimage.data import astronaut
from skimage.segmentation import felzenszwalb, slic, quickshift
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float

segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
segments_slic = slic(img, n_segments=250, compactness=10, 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(16, 8, forward=True)
fig.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)

ax[0].imshow(mark_boundaries(img, segments_fz))
ax[0].set_title("Felzenszwalbs's method")
ax[1].imshow(mark_boundaries(img, segments_slic))
ax[1].set_title("SLIC")
ax[2].imshow(mark_boundaries(img, segments_quick))
ax[2].set_title("Quickshift")
for a in ax:
    a.set_xticks(())
    a.set_yticks(())
plt.show()

Felzenszwalb's number of segments: 2879
Slic number of segments: 247
Quickshift number of segments: 1852

_config.yml

References:

[1.] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, TPAMI, May 2012.
[2.] Adrian Rosebrock, Segmentation: A SLIC Superpixel Tutorial using Python. http://www.pyimagesearch.com/2014/07/28/a-slic-superpixel-tutorial-using-python
[3.] Felzenszwalb, P.F. and Huttenlocher, D.P., Efficient graph-based image segmentation, International Journal of Computer Vision, 2004
[4.] A. Vedaldi and S. Soatto, Quick shift and kernel methods for mode seeking, European Conference on Computer Vision, 2008
[5.] http://ivrg.epfl.ch/research/superpixels#SLICO
[6.] http://scikit-image.org/docs/dev/api/skimage.segmentation.html#skimage.segmentation.slic
Written on March 12, 2015