The Prague Texture Segmentation Datagenerator and Benchmark - Algorithms
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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].
xaos's
GMRF+EM
version 2.0
BIB
WEB
DOC
f1: 0
f2: 0
f3: 0
f4: 0
M. Haindl & S. Mikes:   Gaussian Markov random field model

An efficient and robust type of unsupervised multispectral texture segmentation method is presented. Single decorrelated monospectral texture factors are assumed to be represented by a set of local Gaussian Markov random field (GMRF) models evaluated for each pixel centered image window and for each spectral band. The segmentation algorithm based on the underlying Gaussian mixture (GM) model operates in the decorrelated GMRF parametric space. The algorithm starts with an oversegmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached.

List of uploaded results for 'GMRF+EM' 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] GMRF+EM 2.0 31.93 53.27 11.24 14.97 16.91 33.61 100.00 57.91 63.51 89.26 36.49 3.14 68.41 57.42 4.86 71.80 16.04 7.31 48.88 40.15 63.51 53.52 59.43 20.63 70.55 10.20 19.02 69.24 15.27 53.30 46.11 61.88 64.08 75.46 57.53 9.46