This example compares four popular low-level image segmentation methods. Asit is difficult to obtain good segmentations, and the definition of “good”often depends on the application, these methods are usually used for obtainingan oversegmentation, also known as superpixels. These superpixels then serve asa basis for more sophisticated algorithms such as conditional random fields(CRF).

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Felzenszwalb’s efficient graph based segmentation¶

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This fast 2D image segmentation algorithm, proposed in 1 is popular in thecomputer vision community.The algorithm has a single scale parameter that influences the segmentsize. The actual size and number of segments can vary greatly, depending onlocal contrast.

Efficient graph-based image segmentation, Felzenszwalb, P.F. andHuttenlocher, D.P. International Journal of Computer Vision, 2004

Quickshift image segmentation¶

Quickshift is a relatively recent 2D image segmentation algorithm, based on anapproximation of kernelized mean-shift. Therefore it belongs to the family oflocal mode-seeking algorithms and is applied to the 5D space consisting ofcolor information and image location 2.

One of the benefits of quickshift is that it actually computes ahierarchical segmentation on multiple scales simultaneously.

Quickshift has two main parameters: sigma controls the scale of the localdensity approximation, max_dist selects a level in the hierarchicalsegmentation that is produced. There is also a trade-off between distance incolor-space and distance in image-space, given by ratio.

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 andimage location and is therefore closely related to quickshift. As theclustering method is simpler, it is very efficient. It is essential for thisalgorithm to work in Lab color space to obtain good results. The algorithmquickly gained momentum and is now widely used. See 3 for details. Thecompactness parameter trades off color-similarity and proximity, as in thecase of Quickshift, while n_segments chooses the number of centers forkmeans.

Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,Pascal Fua, and Sabine Suesstrunk, SLIC Superpixels Compared toState-of-the-art Superpixel Methods, TPAMI, May 2012.

Compact watershed segmentation of gradient images¶

Instead of taking a color image as input, watershed requires a grayscalegradient image, where bright pixels denote a boundary between regions.The algorithm views the image as a landscape, with bright pixels forming highpeaks. This landscape is then flooded from the given markers, until separateflood basins meet at the peaks. Each distinct basin then forms a differentimage segment. 4

As with SLIC, there is an additional compactness argument that makes itharder for markers to flood faraway pixels. This makes the watershed regionsmore regularly shaped. 5

Peer Neubert & Peter Protzel (2014). Compact Watershed andPreemptive SLIC: On Improving Trade-offs of Superpixel SegmentationAlgorithms. ICPR 2014, pp 996-1001. DOI:10.1109/ICPR.2014.181https://www.tu-chemnitz.de/etit/proaut/publications/cws_pSLIC_ICPR.pdf

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