Tool NameSuperpixel Segmentation
Tool ID4
Library IDimagery_segmentation
Version1.0
Author(s)O.Conrad (c) 2019

Description

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

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2010): Slic Superpixels. EPFL Technical Report no. 149300, June 2010. epfl.ch

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, 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

SLIC at VLFeat.org

Parameters

NameTypeIdentifierDescriptionConstraints
Input
Featuresgrid list, inputFEATURES
Output
Segmentsshapes, outputPOLYGONS
Superpixelsgrid list, output, optionalSUPERPIXELS
Options
NormalizebooleanNORMALIZEDefault: 0
Maximum Iterationsinteger numberMAX_ITERATIONSMinimum: 1
Default: 100
Regularizationfloating point numberREGULARIZATIONMinimum: 0.000000
Default: 1.000000
Region Sizeinteger numberSIZEStarting 'cell size' of the superpixels given as number of cells.Minimum: 1
Default: 10
Minimum Region Sizeinteger numberSIZE_MINIn postprocessing join segments, which cover less cells than specified here, to a larger neighbour segment.Minimum: 1
Default: 1
Create Superpixel GridsbooleanSUPERPIXELS_DODefault: 0
Post-ProcessingchoicePOSTPROCESSINGAvailable Choices:
[0] none
[1] unsupervised classification
Default: 0
Number of Clustersinteger numberNCLUSTERMinimum: 2
Default: 12
Split ClustersbooleanSPLIT_CLUSTERSDefault: 1