Editing Image segmentation (section)

== Clustering methods ==
{{Main|Data clustering}}
{{multiple image
<!-- Essential parameters -->
| align     = right
| direction = vertical
| width     = 300
| image1    = Aurora borealis over Eielson Air Force Base, Alaska.jpg
| alt1      = Original image
| caption1  = Source image.
| image2    = Polarlicht 2 kmeans 16 large.png
| alt2      = Processed image
| caption2  = Image after running ''k''-means with ''k = 16''. Note that a common technique to improve performance for large images is to downsample the image, compute the clusters, and then reassign the values to the larger image if necessary.
}}
The [[K-means algorithm]] is an [[iterative]] technique that is used to [[Cluster analysis|partition an image]] into ''K'' clusters.<ref>{{cite journal | last1 = Barghout | first1 = Lauren | last2 = Sheynin | first2 = Jacob | year = 2013 | title = Real-world scene perception and perceptual organization: Lessons from Computer Vision | journal = Journal of Vision | volume = 13 | issue = 9| page = 709 | doi=10.1167/13.9.709| doi-access = free }}</ref> The basic [[algorithm]] is

# Pick ''K'' cluster centers, either [[random]]ly or based on some [[heuristic]] method, for example [[K-means++]]
# Assign each pixel in the image to the cluster that minimizes the [[distance]] between the pixel and the cluster center
# Re-compute the cluster centers by averaging all of the pixels in the cluster
# Repeat steps 2 and 3 until convergence is attained (i.e. no pixels change clusters)

In this case, [[distance]] is the squared or absolute difference between a pixel and a cluster center. The difference is typically based on pixel [[Hue|color]], [[Brightness|intensity]], [[Texture (computer graphics)|texture]], and location, or a weighted combination of these factors. ''K'' can be selected manually, [[random]]ly, or by a [[heuristic]]. This algorithm is guaranteed to converge, but it may not return the [[Global optimum|optimal]] solution. The quality of the solution depends on the initial set of clusters and the value of ''K''.

The [[Mean shift|Mean Shift]] algorithm is a technique that is used to partition an image into an unknown [[A priori and a posteriori|apriori]] number of clusters. This has the advantage of not having to start with an initial guess of such parameter which makes it a better general solution for more diverse cases.