An images segmented into super pixels. Example from the paper. |

After searching for an easy algorithm to compute super pixels I found a cool method segment images based on color using a modified K-Means version called

**Simple Linear Iterative Clustering**or SLIC. The input to the algorithm is a set of vectors describing each pixel in the images as a vector:

v = [l a b x y]

The first three entries represent the color in LAB space. The last two the position in the image. The goal of the algorithm is to segment the image into

*k*more or less equally sized regions of same color, called super pixels. The algorithm is initialized by sampling the image k times uniformly. Based on these initial centers one runs a local K-Means for each center. So a clusters influence is in a limited region.
The other novelty of the algorithm is the distance measure that scales the color and position.

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