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].
scarpa's
TFR
BIB
WEB
DOC
f1: 0
f2: 1
f3: 0
f4: 0
G. Scarpa & M. Haindl:   Texture Fragmentation and Reconstruction

The Texture Fragmentation and Reconstruction (TFR) segmentation algorithm is based on a texture modeling particularly suited for segmentation in an unsupervised framework. A texture is regarded for each fixed spatial direction as a finite-state Markov chain where the states of the process are quantized colors. On the basis of this modeling, a simple segmentation algorithm is derived that precesses independently color and spatial information, by first performing a color-based clustering, which provides the quantized colors, and then by means of a further spatial-based clustering, which separates regions according to their transition probability profile. Finally, a region merging algorithm allows to recover the different textures, that is to recompose their internal Markov chains.


List of uploaded results for 'TFR' 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] TFR 0 46.13 2.37 23.99 26.70 25.23 28.73 12.50 61.32 73.00 68.91 27.00 8.56 68.62 59.76 8.61 69.73 15.51 12.03 37.29 33.80 73.00 68.48 64.83 18.21 68.54 7.73 25.28 68.13 17.47 57.90 55.57 68.82 70.72 61.21 84.40 8.05