The Prague Texture Segmentation Datagenerator and Benchmark - Algorithms
optimized
for

The concise description of algorithms contains hyperlinks to further information (author, algorithms details, BIB entry, WWW external page).
The algorithm features are: f1 = classification (supervised segmentation), f2 = hiearchy result (manual selection), f3 = known number of regions, f4 = [reserved].
sylvia's
TEX-ROI-SEG
version 1.0
BIB
WEB
f1: 0
f2: 0
f3: 0
f4: 0
Michael Donoser and Horst Bischof:   Texture ROI-Segmentation

Defaut Parametrization

[1] Donoser, M. and Bischof, H. (2008). Using Covariance Matrices for Unsupervised Texture Segmentation. In Proceedings of International Conference on Pattern Recognition (ICPR) , Tampa, USA.

[2] Donoser, M. and Bischof, H. (2007). ROI-SEG: Unsupervised color segmentation by combining differently focused sub results. In Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA.

Implementation by Sylwia Steginska

List of uploaded results for 'TEX-ROI-SEG' algorithm
   benchmark label version CS OS US ME NE O C CA CO CC I II EA MS RM CI GCE LCE BCE GBCE BGM SC SSC VD L AVI NVI NMI M ARI JC DC FMI WI WII NBDE
Colour [normal] TEX-ROI-SEG 1.0 56.37 11.93 19.79 11.55 10.29 18.21 9.63 69.45 78.26 81.24 21.74 4.16 76.31 68.88 7.37 77.86 11.99 6.71 30.48 25.20 78.26 73.70 72.48 13.66 76.83 5.74 18.34 78.12 11.74 68.74 63.73 75.91 77.00 73.11 83.24 5.45