Tool Superpixel Segmentation

The Superpixel Segmentation tool implements the 'Simple Linear Iterative Clustering' (SLIC) algorithm, an image segmentation method described in Achanta et al. (2010).

SLIC is a simple and efficient method to decompose an image in visually homogeneous regions. It is based on a spatially localized version of k-means clustering. Similar to mean shift or quick shift, each pixel is associated to a feature vector.

This tool is follows the SLIC implementation created by Vedaldi and Fulkerson as part of the VLFeat library.

References

SLIC at VLFeat.org
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & SÃ¼sstrunk, S. (2010): Slic Superpixels. EPFL Technical Report no. 149300, June 2010. epfl.ch.
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & SÃ¼sstrunk, S. (2012): SLIC Superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence, 34(11), 2274-2282. ieee.org.

Author: O.Conrad (c) 2019
Menu: Imagery|Segmentation

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Features	Grid list, input	`FEATURES`	-	-
Output	Segments	Shapes, output	`POLYGONS`	-	-
Output	Superpixels (*)	Grid list, output, optional	`SUPERPIXELS`	-	-
Options	Grid System	Grid system	`PARAMETERS_GRID_SYSTEM`	-	-
	Normalize	Boolean	`NORMALIZE`	-	Default: 0
	Maximum Iterations	Integer	`MAX_ITERATIONS`	-	Minimum: 1 Default: 100
	Regularization	Floating point	`REGULARIZATION`	-	Minimum: 0.000000 Default: 1.000000
	Region Size	Integer	`SIZE`	Starting 'cell size' of the superpixels given as number of cells.	Minimum: 1 Default: 10
	Minimum Region Size	Integer	`SIZE_MIN`	In postprocessing join segments, which cover less cells than specified here, to a larger neighbour segment.	Minimum: 1 Default: 1
	Create Superpixel Grids	Boolean	`SUPERPIXELS_DO`	-	Default: 0
	Post-Processing	Choice	`POSTPROCESSING`	-	Available Choices: [0] none [1] unsupervised classification Default: 0
	Number of Clusters	Integer	`NCLUSTER`	-	Minimum: 2 Default: 12
	Split Clusters	Boolean	`SPLIT_CLUSTERS`	-	Default: 1
(*) optional

Command-line

Usage: saga_cmd imagery_segmentation 4 [-FEATURES <str>] [-NORMALIZE <str>] [-POLYGONS <str>] [-MAX_ITERATIONS <num>] [-REGULARIZATION <double>] [-SIZE <num>] [-SIZE_MIN <num>] [-SUPERPIXELS_DO <str>] [-SUPERPIXELS <str>] [-POSTPROCESSING <str>] [-NCLUSTER <num>] [-SPLIT_CLUSTERS <str>]
  -FEATURES:<str>         	Features
	Grid list, input
  -NORMALIZE:<str>        	Normalize
	Boolean
	Default: 0
  -POLYGONS:<str>         	Segments
	Shapes, output
  -MAX_ITERATIONS:<num>   	Maximum Iterations
	Integer
	Minimum: 1
	Default: 100
  -REGULARIZATION:<double>	Regularization
	Floating point
	Minimum: 0.000000
	Default: 1.000000
  -SIZE:<num>             	Region Size
	Integer
	Minimum: 1
	Default: 10
  -SIZE_MIN:<num>         	Minimum Region Size
	Integer
	Minimum: 1
	Default: 1
  -SUPERPIXELS_DO:<str>   	Create Superpixel Grids
	Boolean
	Default: 0
  -SUPERPIXELS:<str>      	Superpixels
	Grid list, output, optional
  -POSTPROCESSING:<str>   	Post-Processing
	Choice
	Available Choices:
	[0] none
	[1] unsupervised classification
	Default: 0
  -NCLUSTER:<num>         	Number of Clusters
	Integer
	Minimum: 2
	Default: 12
  -SPLIT_CLUSTERS:<str>   	Split Clusters
	Boolean
	Default: 1