11.8. Simple Linear Iterative Clustering (SLIC)

11.8. Simple Linear Iterative Clustering (SLIC)
	11. Künstlerisch

11.8. Simple Linear Iterative Clustering (SLIC)

11.8.1. Wirkungsweise

This filter creates superpixels based on k-means clustering.

Superpixels are small cluster of pixels that share similar properties. Superpixels simplifies images with a great number of pixels making them more easy to be treated in many domains (computer vision, pattern recognition and machine intelligence). GIMP's aim is more humble: create a posterization effect.

k-means clustering is one of the most used algorithms to create superpixels. Superpixel color is the mean of pixels color in the corresponding region.

11.8.2. Filteraufruf

This filter is found in the image window menu under Filters → Artistic → Simple Linear Iterative Clustering….

11.8.3. Eigenschaften

Abbildung 17.211. „Simple Linear Iterative Clustering“ options

„Simple Linear Iterative Clustering“ options

Presets, „Input Type“, Clipping, Blending Options, Preview, Split view

	Anmerkung
	These options are described in Abschnitt 2, „Gemeinsame Funktionsmerkmale“.

Regions size

Increasing regions size collects more pixels, and so superpixels size increases also.

Abbildung 17.212. „Regions size“ example

„Regions size“ example

Regions size = 16

„Regions size“ example

Regions size = 32

Compactness

Superpixels borders may be irregular. Increasing this option gives superpixels more regular border.

Abbildung 17.213. „Compactness“ example

„Compactness“ example

Compactness = 20

„Compactness“ example

Compactness = 40: look at the dome.

Iterations

How many times filter is repeated. Increasing this value gives more details.

Abbildung 17.214. „Regions size“ example

„Regions size“ example

Iterations = 1 (default)

„Regions size“ example

Iterations = 15


11.7. Fotokopie		11.9. Warmes Leuchten

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